Camera equipment control method, equipment and storage medium

Abstract

The invention discloses a camera equipment control method, equipment and a storage medium, and relates to the technical field of data processing. In the method, a control module of camera equipment firstly establishes wireless connection with wireless access equipment, determines channel state information based on a received wireless signal, and further determines an amplitude value corresponding to each subcarrier at each sampling moment according to the channel state information; therefore, a target dynamic index corresponding to the wireless channel can be determined according to the amplitude value, and when the numerical relationship between the target dynamic index and a first preset threshold value is not matched with the current operation mode of the camera equipment, the camera equipment is controlled to execute a mode switching action. Therefore, the purpose of controlling the video monitoring starting opportunity based on low-energy-consumption wireless communication is achieved, and the energy consumption of the camera equipment is effectively reduced.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to camera equipment control methods, devices, and storage media. Background Technology

[0002] Currently, video cameras primarily rely on video image data to determine environmental conditions or target movement. This means they must continuously acquire images and analyze video streams to detect environmental changes or moving targets. This "always vigilant" working mode means that even when the monitored area is stationary for extended periods without a target, the imaging, encoding, transmission, and analysis modules must continue operating, resulting in high power consumption.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this application is to provide a camera device control method, device, and storage medium, which aims to solve the technical problem of how to reduce the power consumption of camera devices.

[0005] To achieve the above objectives, this application proposes a camera equipment control method, the camera equipment control method comprising:

[0006] Establish a wireless connection with the wireless access device and determine channel state information based on the received wireless signals; The amplitude value of each subcarrier at each sampling time is determined based on the channel state information. The multidimensional amplitude characteristics are determined based on the amplitude values, and the target dynamic indicators corresponding to the wireless channel are determined based on the multidimensional amplitude characteristics. When the numerical relationship between the target dynamic indicator and the first preset threshold does not match the current operating mode of the camera device, the camera device is controlled to perform a mode switching action, wherein the camera device is currently in a sleep mode or a working mode.

[0007] In one embodiment, the channel state information is a three-dimensional complex tensor describing the channel coefficients corresponding to each subcarrier, each transmit antenna, and each receive antenna. The step of determining the amplitude value of each subcarrier at each sampling time based on the channel state information includes: The absolute value of each channel coefficient in the three-dimensional complex tensor is determined as the amplitude value corresponding to the channel coefficient; The amplitude values ​​of the transmitting and receiving antennas on the same subcarrier are aggregated to obtain the amplitude value of each subcarrier at each sampling time.

[0008] In one embodiment, the multidimensional amplitude features include amplitude variation intensity features, time variance features, time correlation features, and principal component variation features; The steps of determining multi-dimensional amplitude features based on the amplitude value and determining the target dynamic index corresponding to the wireless channel based on the multi-dimensional amplitude features include: Within a preset time window, the mean square value of the change in amplitude value corresponding to each subcarrier is determined, and the mean square values ​​corresponding to each subcarrier are aggregated to obtain the amplitude change intensity feature; Within the preset time window, the variance of the amplitude value corresponding to each subcarrier is determined, and the variances corresponding to each subcarrier are aggregated to obtain the time variance feature; The amplitude values ​​corresponding to each subcarrier are aggregated to obtain an amplitude sequence, and the correlation coefficient between the amplitude sequences corresponding to two different preset time windows is determined, and the correlation coefficient is used as the time correlation feature. An amplitude-time matrix is ​​constructed based on the amplitude values ​​corresponding to each subcarrier. Principal component analysis is performed on the amplitude-time matrix to obtain principal component eigenvalues. The sum of the principal component eigenvalues ​​or the proportion of the sum of the principal component eigenvalues ​​to the sum of all eigenvalues ​​is used as the principal component variation feature. At least two of the following features—the amplitude variation intensity feature, the time variance feature, the time correlation feature, and the principal component variation feature—are used as the multidimensional amplitude feature; The target dynamic index is obtained by weighted summation of the multidimensional amplitude features.

[0009] In one embodiment, the operating mode includes a first operating mode and a second operating mode. The step of controlling the camera device to perform a mode switching action when the numerical relationship between the target dynamic indicator and a first preset threshold does not match the current operating mode of the camera device includes: When the target dynamic index is less than the first preset threshold, the camera device is controlled to switch to the sleep mode; When the target dynamic index is greater than or equal to the first preset threshold, the camera device is controlled to switch to the first working mode to control the camera device to acquire images and detect the images to determine whether there is a target object in the images; If the target object exists in the image, the camera device is controlled to switch to the second working mode; If the target object is not present in the image, the camera device is controlled to switch to the sleep mode, wherein the image acquisition frame rate corresponding to the first working mode is less than the image acquisition frame rate corresponding to the second working mode.

[0010] In one embodiment, after the step of controlling the camera device to switch to the second working mode if the target object exists in the image, the method further includes: Determine the bounding box of the target object in the image, and use the center coordinates of the bounding box as the position coordinates of the target object; Determine the distance between the position coordinates of the target object in adjacent image frames; Determine the variance of the position coordinates of the target object in a preset number of image frames; Determine the size change of the bounding box corresponding to the target object in adjacent image frames; The tracking confidence score is obtained by weighted summation of the distance, the variance, and the size change. When the tracking confidence level is less than or equal to a preset confidence threshold, the tracking status of the camera device is determined based on the target dynamic index and the tracking confidence level.

[0011] In one embodiment, the step of determining the tracking status of the camera device based on the target dynamic index and the tracking confidence level includes: When the tracking confidence level is less than or equal to the preset confidence threshold and the target dynamic index is greater than the second preset threshold, the camera device is controlled to maintain tracking. When the tracking confidence is less than or equal to the preset confidence threshold and the target dynamic index is less than the third preset threshold, the camera device is controlled to stop tracking, wherein the second preset threshold is greater than the third preset threshold and the third preset threshold is greater than the first preset threshold.

[0012] In one embodiment, after the step of controlling the camera device to maintain tracking, the method further includes: The detection range and detection step size of the camera device for the image are determined based on the target dynamic index, and the detection range and the detection step size are proportional to the target dynamic index. The magnitude of the preset reliability threshold is determined based on the target dynamic index, and the magnitude of the preset reliability threshold is inversely proportional to the target dynamic index.

[0013] In one embodiment, after the step of controlling the target tracking unit to stop tracking, the method further includes: Determine the increment of the target dynamic indicator within a preset time period. If the increment is greater than a preset increment threshold, re-control the camera device to track.

[0014] In addition, to achieve the above objectives, this application also proposes a camera device control device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the camera device control method as described above.

[0015] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the camera device control method described above.

[0016] This application provides a camera device control method. In this method, the camera device control module first establishes a wireless connection with a wireless access device and determines the channel state information based on the received wireless signal. Then, it determines the amplitude value of each subcarrier at each sampling time based on the channel state information, so that the target dynamic index corresponding to the wireless channel can be determined based on the amplitude value. When the numerical relationship between the target dynamic index and a first preset threshold does not match the current operating mode of the camera device, the camera device is controlled to perform a mode switching action.

[0017] The aforementioned method is based on the reality that the propagation space of wireless signals overlaps with the monitoring space of the camera equipment. It relies on the influence of moving objects within the propagation space on the signal propagation process to determine whether a monitoring need exists within the camera equipment's monitoring range. Then, when the need differs from the actual mode, a mode switch is performed. Therefore, it achieves the effect of controlling the camera equipment to enter sleep mode when there is no monitoring need, and promptly activating the camera equipment to monitor when there is a need. This achieves the goal of controlling the timing of video monitoring activation based on low-power wireless communication, effectively reducing the energy consumption of the camera equipment. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the control module provided in Embodiment 1 of the camera equipment control method of this application; Figure 2 This is a flowchart illustrating an embodiment of the camera device control method of this application. Figure 3 This is a flowchart illustrating Embodiment 2 of the camera equipment control method of this application; Figure 4 This is a flowchart illustrating Embodiment 3 of the camera equipment control method of this application; Figure 5 This is a schematic diagram of the control flow provided in Embodiment 3 of the camera equipment control method of this application; Figure 6 This is a schematic diagram of the hardware operating environment involved in the camera device control method in the embodiments of this application.

[0021] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of this application and are not intended to limit this application.

[0023] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific embodiments. It should be noted that all actions involving the acquisition of signals, information, or data in this application are performed in accordance with the relevant data protection laws and regulations of the country where the application is located, and with authorization from the owner of the corresponding device.

[0024] Currently, video cameras primarily rely on video image data to determine environmental conditions or target movement. This means they must continuously acquire images and analyze video streams to detect environmental changes or moving targets. This "always-on" working mode means that even when the monitored area is stationary and targetless for extended periods, the imaging, encoding, transmission, and analysis modules must continue operating, resulting in high power consumption. For cameras powered by batteries or energy storage, this sustained high power consumption significantly shortens battery life, increases charging and maintenance frequency, and drastically reduces the camera's lifespan.

[0025] In view of the above problems, this application proposes a camera device control method. In this method, the control module of the camera device first establishes a wireless connection with the wireless access device, and determines the channel state information based on the received wireless signal. Then, it determines the amplitude value of each subcarrier at each sampling time based on the channel state information, so as to determine the target dynamic index corresponding to the wireless channel based on the amplitude value. When the numerical relationship between the target dynamic index and the first preset threshold does not match the current operating mode of the camera device, the camera device is controlled to perform a mode switching action.

[0026] The aforementioned method is based on the reality that the propagation space of wireless signals overlaps with the monitoring space of the camera equipment. It relies on the influence of moving objects within the propagation space on the signal propagation process to determine whether a monitoring need exists within the camera equipment's monitoring range. Then, when the need differs from the actual mode, a mode switch is performed. Therefore, it achieves the effect of controlling the camera equipment to enter sleep mode when there is no monitoring need, and promptly activating the camera equipment to monitor when there is a need. This achieves the goal of controlling the timing of video monitoring activation based on low-power wireless communication, effectively reducing the energy consumption of the camera equipment.

[0027] First Embodiment The first embodiment of this application provides a camera device control method, applied to the control module of a camera device. (See also...) Figure 1 In this embodiment, the control module includes a wireless signal communication unit, a channel state acquisition unit, a channel state analysis unit, an image acquisition unit, a target tracking unit, and a camera control unit.

[0028] The system includes the following components: a wireless signal communication unit for establishing a wireless connection with the wireless access device and receiving wireless signals transmitted by the device; a channel state acquisition unit for determining channel state information based on the received wireless signals; a channel state analysis unit for determining the amplitude value of each subcarrier at each sampling time based on the channel state information, then determining multi-dimensional amplitude characteristics based on these amplitude values, and finally determining the target dynamic indicators corresponding to the wireless channel based on these multi-dimensional amplitude characteristics; a camera control unit for controlling the camera device to perform mode switching actions and controlling operating parameters such as image acquisition frame rate, zoom exposure, and pitch yaw; an image acquisition unit for acquiring images; and a target tracking unit for detecting and tracking target objects from the images.

[0029] Based on this, refer to Figure 2 The camera device control method provided in this embodiment includes steps S10 to S40: Step S10: Establish a wireless connection with the wireless access device and determine the channel state information based on the received wireless signal.

[0030] Wireless signals are electromagnetic wave signals radiated by the transmitting antennas of wireless access devices such as wireless routers or dedicated beacons. These signals carry data transmission on a specific radio frequency carrier frequency and are received by the wireless signal communication unit of the camera equipment.

[0031] For example, the steps of establishing a wireless connection with the wireless access device and determining channel state information based on the received wireless signal specifically include: the wireless signal communication unit receiving the wireless signal sent by the wireless access device and converting the wireless signal into a digital baseband signal. Based on the channel frequency response of the pilot subcarrier position in the digital baseband signal, the channel state information corresponding to the wireless signal is determined.

[0032] It should be noted that the digital baseband signal is a binary sequence obtained after the aforementioned wireless signal has undergone a series of physical layer reception and processing, such as antenna sensing, radio frequency front-end amplification, downconversion and analog-to-digital conversion. Its center frequency has been reduced to baseband, and the data it carries is the same as the original wireless signal.

[0033] Furthermore, the wireless signal communication unit includes a receiving antenna, a radio frequency front-end, and a baseband processing unit. The specific steps of the wireless signal communication unit receiving wireless signals transmitted by the wireless access device and converting the wireless signals into digital baseband signals include: First, the receiving antenna of the wireless signal communication unit acts as a sensor. Its vibrator generates an induced current under the influence of an electromagnetic field, converting the electromagnetic wave signal in space, i.e., the aforementioned wireless signal, into an analog electrical signal of the corresponding frequency, which is then input to the radio frequency (RF) front-end. Next, the RF front-end's bandpass filter removes noise and interference outside the operating frequency band from the analog electrical signal, and the low-noise amplifier in the RF front-end performs preliminary amplification of the filtered analog signal to obtain an amplified RF analog signal, thus improving signal strength. Then, the local oscillator in the RF front-end generates a fixed-frequency local oscillator signal. This amplified RF analog signal is mixed with the local oscillator signal in a mixer to down-convert the high-frequency RF analog signal to baseband, resulting in a down-converted analog signal, which is then input to the baseband processing unit. Finally, the analog-to-digital converter in the baseband processing unit samples the down-converted analog signal at a preset sampling rate and quantizes the amplitude value of each sample point into binary digits to obtain a digital baseband signal.

[0034] It should be noted that pilot subcarriers are inserted at fixed subcarrier positions by wireless access devices in Orthogonal Frequency Division Multiplexing (OFDM) systems. The receiving end knows the transmitted signal content in advance for channel assessment. Channel frequency response refers to the mathematical description of the effect of the wireless channel on the signal in the frequency domain. It is usually a complex number, where the amplitude represents the channel's gain or attenuation of that frequency component, and the phase represents the introduced phase shift. Channel state information is the set of channel frequency responses on all OFDM subcarriers, which can be represented as a complex matrix or tensor containing amplitude and phase information.

[0035] Furthermore, the steps for determining the channel state information corresponding to the wireless signal based on the channel frequency response of the pilot subcarrier position in the digital baseband signal specifically include: first, performing time and frequency synchronization on the digital baseband signal to determine the starting position of each orthogonal frequency division multiplexing (OFDM) symbol; then, removing the cyclic prefix before each OFDM symbol to obtain valid OFDM time-domain symbol data, i.e., the digital baseband signal frame; next, performing a Fourier transform on each OFDM time-domain symbol data to obtain OFDM frequency-domain symbol data; extracting the received symbol at the pilot subcarrier position from the OFDM frequency-domain symbol data; determining the channel frequency response of the pilot subcarrier position based on the received symbol; and determining the aforementioned channel state information based on the channel frequency response of the pilot subcarrier position.

[0036] Time synchronization refers to the process of determining the start time of each orthogonal frequency division multiplexing (OFDM) symbol on the time axis at the receiving end. Because signal propagation in the channel introduces delays, and there is a clock discrepancy between the transmitting end and the camera equipment, the continuous signal stream received by the camera equipment must be synchronized to find the correct start of each OFDM symbol before subsequent demodulation can proceed.

[0037] As a first option for time synchronization, a synchronization sequence such as a preamble stored locally in the camera device can be obtained. The aforementioned digital baseband signal is cross-correlated with the synchronization sequence. When the received digital baseband signal matches the synchronization sequence, a peak will appear in the cross-correlation result. The position of this peak is the starting point of the orthogonal frequency division multiplexing symbol.

[0038] As a second time synchronization option, the characteristic that the cyclic prefix is ​​a copy of the last segment of the valid data can be utilized. In the received digital baseband signal, autocorrelation is performed on the sub-signals within two windows separated by one valid symbol length. When the contents of these two sub-signals are identical, a peak value will appear in the autocorrelation value; the location of this peak value is the starting point of the orthogonal frequency division multiplexing symbol.

[0039] The aforementioned frequency synchronization refers to the process of estimating and compensating for the carrier frequency deviation between the digital baseband signal and the local oscillator. Specifically, the frequency synchronization steps include: calculating the phase difference between two consecutive identical synchronization sequences in the digital baseband signal, where the phase difference is proportional to the time interval between the two synchronization sequences and the carrier frequency deviation. Based on this phase difference and time interval, the frequency deviation value can be estimated. This frequency deviation value is then converted into a phase rotation factor. In the time domain, the sampled sequence of the time-synchronized digital baseband signal is multiplied point-by-point by the conjugate of this rotation factor to rotate the phase of the digital baseband signal in the reverse direction, thereby correcting the carrier frequency deviation in the digital domain.

[0040] Furthermore, after completing the aforementioned time and frequency synchronization, a time-domain sampled data block of a single orthogonal frequency division multiplexing (OFDM) symbol in the baseband signal can be obtained. This sampled data block contains a cyclic prefix and valid transmitted data. Based on the known cyclic prefix length of the communication protocol, starting from the beginning of each located OFDM symbol data block, the sampled points whose initial length is equal to the cyclic prefix are discarded, thus obtaining the valid OFDM time-domain symbol data. A Fourier transform is then performed on the aforementioned OFDM time-domain symbol data to obtain the OFDM frequency-domain symbol data.

[0041] Furthermore, after obtaining the orthogonal frequency division multiplexing (OFDM) frequency domain symbol data, the steps of extracting the received symbols at the pilot subcarrier positions from the OFDM frequency domain symbol data, determining the channel frequency response at the pilot subcarrier positions based on the received symbols, and determining the channel state information based on the channel frequency response at the pilot subcarrier positions specifically include: Based on the index positions of the pilot subcarriers specified in the communication protocol, the received symbols located at the aforementioned index positions are extracted from the orthogonal frequency division multiplexing (OFDM) frequency domain symbol data, and these received symbols are compared with the locally stored known transmitted pilot symbol sequence. Channel estimation algorithms, such as least squares estimation, are used to calculate the channel frequency response at each pilot subcarrier position, i.e., to solve for the complex ratio of the received symbol to the known transmitted pilot symbol. Next, using interpolation algorithms, including linear interpolation or spline interpolation, the channel frequency responses at the aforementioned pilot subcarrier positions are used as known samples to estimate the channel frequency responses at all subcarrier positions within the entire OFDM frequency domain symbol data, obtaining the channel frequency responses of all subcarriers. The channel frequency responses of all subcarriers are then arranged according to their corresponding subcarrier index order to construct the channel state information.

[0042] Step S20: Determine the amplitude value of each subcarrier at each sampling time based on the channel state information.

[0043] It should be noted that the channel state information mentioned above is represented by a three-dimensional complex tensor. The dimension of this three-dimensional complex tensor is the three-dimensional complex tensor describing the channel coefficients corresponding to each subcarrier, each transmit antenna, and each receive antenna. For example, at a certain sampling time t, the channel state information provided by the channel state acquisition unit can be represented as H_t, with dimensions (S, Tx, Rx). Here, S represents the number of subcarriers, Tx represents the number of transmit antennas, and Rx represents the number of receive antennas.

[0044] Therefore, step S20 above includes steps S21 to S22: Step S21: Determine the absolute value of each channel coefficient in the three-dimensional complex tensor as the amplitude value corresponding to the channel coefficient.

[0045] For example, the magnitude of each channel coefficient h(s,tx,rx) in the complex tensor H_t is determined, and this magnitude is used as the amplitude value corresponding to each channel coefficient: a(s,tx,rx)=|h(s,tx,rx)|, where a(s,tx,rx) is the amplitude value corresponding to the channel coefficient.

[0046] Step S32: Aggregate the amplitude values ​​of the transmitting antenna and the receiving antenna on the same subcarrier to obtain the amplitude value corresponding to each subcarrier at each sampling time.

[0047] To obtain the comprehensive amplitude representation of each subcarrier s at sampling time t, the transmit antenna (Tx) and receive antenna (Rx) dimensions are aggregated to obtain a one-dimensional amplitude vector a_t. The length of the amplitude vector a_t is the number of subcarriers S. The s-th element in this amplitude vector represents the aggregated amplitude value of subcarrier s at sampling time t across all antenna pairs. The above one-dimensional amplitude vector a_t is collected continuously for T sampling times and arranged in chronological order to form a two-dimensional matrix A[s,t]. The elements in A[s,t] are the amplitude values ​​of each subcarrier s at sampling time t, where T is greater than 1.

[0048] As an alternative to the first aggregation method, the average amplitude value of the same subcarrier s on all (tx, rx) antenna pairs can be calculated, and this average value can be used as the amplitude value of each subcarrier at each sampling time.

[0049] As an alternative to the second aggregation method, the amplitude values ​​of the same subcarrier s on all (tx, rx) antenna pairs can be summed, and this sum can be used as the amplitude value of each subcarrier at each sampling time.

[0050] As a third aggregation option, the maximum / minimum amplitude values ​​of the same subcarrier s across all (tx, rx) antenna pairs are taken as the amplitude values ​​of each subcarrier at each sampling time.

[0051] Step S30: Determine the multidimensional amplitude features based on the amplitude values, and determine the target dynamic index corresponding to the wireless channel based on the multidimensional amplitude features.

[0052] It should be noted that the multidimensional amplitude features are a set of characteristic parameters extracted from different dimensions from the amplitude values ​​corresponding to each subcarrier at each sampling time. These parameters characterize the dynamic change pattern of the wireless channel and are used to quantify the fluctuation of the channel state. The target dynamic index is a comprehensive scalar or vector generated from the multidimensional amplitude features through further processing, such as weighting, fusion, or threshold comparison. It is used to determine whether there is a moving target within the monitoring range of the camera device. This target dynamic index can intuitively characterize the intensity, persistence, and trend of wireless channel disturbances caused by the movement of target objects such as humans and vehicles within the monitoring range.

[0053] Furthermore, the aforementioned multidimensional amplitude features include amplitude change intensity features, time variance features, time correlation features, and principal component change features.

[0054] Therefore, step S30 above includes steps S31 to S36: Step S31: Within a preset time window, determine the mean square value of the change in amplitude value corresponding to each subcarrier, aggregate the mean square values ​​corresponding to each subcarrier, and obtain the amplitude change intensity feature.

[0055] For example, the step of determining the amplitude variation intensity feature specifically includes: assuming that the two-dimensional matrix A[s,t] in step S30 above represents the amplitude value of each subcarrier corresponding to each sampling time, the first-order difference ΔA[s,t] = A[s,t] - A[s,t-1] of A[s,t] can be used as the change in amplitude value of each subcarrier s between consecutive time points. Next, within a preset time window L, the mean square value of the change corresponding to each subcarrier is determined: P[s] = (1 / L) × Σ(ΔA[s,t])², where P[s] is the mean square value of subcarrier s. The mean square values ​​corresponding to each subcarrier are aggregated to obtain the amplitude variation intensity feature.

[0056] If a target object moves suddenly within the monitoring range of the camera equipment, it will cause the amplitude change intensity characteristic value to increase. Therefore, the above amplitude change intensity characteristic can be used to quantify the severity of channel changes.

[0057] Among them, as the first option to aggregate the mean square values ​​corresponding to each subcarrier, the average value of the mean square value P[s] of all subcarriers s can be determined, and this average value can be used as the amplitude variation intensity feature.

[0058] As a second alternative to aggregate the mean square values ​​corresponding to each subcarrier, the mean square values ​​P[s] of all subcarriers s can be summed, and this sum can be used as the amplitude variation intensity feature.

[0059] As a third option for aggregating the mean square values ​​of each subcarrier, the maximum / minimum value among the mean square values ​​P[s] of all subcarriers s is taken as the amplitude variation intensity feature.

[0060] Step S32: Within the preset time window, determine the variance of the amplitude value corresponding to each subcarrier, aggregate the variances corresponding to each subcarrier, and obtain the time variance feature.

[0061] For example, the step of determining the time variance characteristics specifically includes: for each subcarrier s, the channel state analysis unit extracts its amplitude sequence within the aforementioned time window L: {A[s,t-L+1], A[s,t-L+2], ..., A[s,t]}, and calculates the variance of the amplitude sequence using the formula: Var[s]=(1 / (L-1))×Σ Mean_s is the average amplitude value of subcarrier s within the time window L, and the summation Σ is performed for all L time points t_i within the window. Each subcarrier s will obtain a variance value Var[s], which quantifies the degree of fluctuation of the amplitude value of a specific subcarrier within the time window L. The variance values ​​Var[1], Var[2], ..., Var[S] of all subcarriers are aggregated to obtain the above time variance feature.

[0062] The method of aggregating the variance values ​​of all subcarriers can be referred to the method of aggregating the mean square values ​​of each subcarrier in step S31 above, and will not be repeated here.

[0063] The more violent the target object's movement, the larger its range of motion, or the longer its duration, the stronger and more persistent the disturbance to the channel, and the larger the aforementioned time variance characteristic value will be within the corresponding time window. Therefore, the aforementioned time variance characteristic can represent the overall instability of the channel within a specific time period and can reflect the motion state of the target object within the monitoring range.

[0064] Step S33: Aggregate the amplitude values ​​corresponding to each subcarrier to obtain an amplitude sequence, and determine the correlation coefficient between the amplitude sequences corresponding to two different preset time windows, and use the correlation coefficient as the time correlation feature.

[0065] For example, the steps for determining the time correlation characteristics specifically include: aggregating the amplitude values ​​of all subcarriers at the same sampling time t to form a one-dimensional time series X[t], which characterizes the overall channel amplitude state within the monitoring range corresponding to sampling time t. The aggregation method can refer to the method used in step S31 above to aggregate the mean square values ​​corresponding to each subcarrier. Next, two adjacent or partially overlapping time windows L1 and L2 of preset length are determined. Time window L1 contains L X values ​​from time point tL to t-1, and time window L2 contains L X values ​​from time point tk to t, where k is the offset and k≧0. Finally, the correlation coefficient between the time series corresponding to time window L1 and the time series corresponding to time window L2 is calculated to obtain the aforementioned time correlation characteristics. The aforementioned correlation coefficients include, but are not limited to, Pearson correlation coefficient and Spearman correlation coefficient.

[0066] When a target object moves continuously within the monitoring range, its disturbance to the multipath channel is continuous and evolves gradually. This causes the time series X[t], which characterizes the overall channel amplitude state, to exhibit a coherent and smooth trend over a period of time. In this case, the sequence change patterns within two adjacent or partially overlapping time windows of a preset length will be highly similar, and the calculated correlation coefficients will also be high. Therefore, the aforementioned time correlation characteristics can be used to determine whether channel changes are persistent.

[0067] Step S34: Construct an amplitude-time matrix based on the amplitude values ​​corresponding to each subcarrier, perform principal component analysis on the amplitude-time matrix to obtain principal component eigenvalues, and use the sum of the principal component eigenvalues ​​or the proportion of the sum of the principal component eigenvalues ​​to the sum of all eigenvalues ​​as the principal component variation feature.

[0068] For example, the steps for determining the principal component variation characteristics specifically include: constructing a data matrix X[S,T] based on the amplitude values ​​of S subcarriers at T consecutive sampling times. Each row of the data matrix X[S,T] represents the amplitude value of one subcarrier at each sampling time, and each column of the data matrix X[S,T] represents the amplitude values ​​of all subcarriers at the same sampling time. The amplitude values ​​of each row of the data matrix X[S,T], i.e., each subcarrier at each sampling time, are centered to obtain a centered data matrix X_c[S,T]. Then, the covariance matrix C of the centered data matrix X_c[S,T] is calculated: C = (1 / (T-1)) × (X_c × ... ),in This is the transpose of X_c. Each element C[i,j] of the covariance matrix C represents the correlation between the amplitude changes of the i-th and j-th subcarriers. Eigenvalue decomposition is performed on the covariance matrix C. The equation C×v=λ×v is solved, where λ is the eigenvalue, v is the eigenvector corresponding to the eigenvalue, and each eigenvector v is an S-dimensional vector representing a principal component direction of the data in S-dimensional space. The eigenvalue λ corresponding to the eigenvalue represents the variance of the data in that principal component direction. Based on the magnitude of the eigenvalues, the K largest eigenvalues ​​are selected as the principal component eigenvalues. The sum of the principal component eigenvalues ​​or the proportion of the sum of the principal component eigenvalues ​​to the sum of all eigenvalues ​​is used as the principal component variation characteristic.

[0069] Furthermore, the above-mentioned centering process includes: calculating the mean amplitude value of each subcarrier at each sampling time, and then subtracting this mean from the amplitude value of each subcarrier at each sampling time to obtain the centered data matrix X_c[S,T]. The formula can be expressed as: X_c[i,T]=X[i,T]-mean(X[i,T]), where mean(X[i,T]) represents the mean amplitude value of the i-th subcarrier at each sampling time. After the above centering process, the mean of each row of the data matrix X_c[S,T] is 0. Centering eliminates the static deviation between the amplitude values ​​of different subcarriers at the same sampling time, allowing subsequent principal component analysis to focus on the amplitude variation component.

[0070] The movement of a target object within the monitoring range will affect multiple propagation paths through reflection or diffraction. Therefore, the channel disturbances caused by the movement of the target object are strongly correlated and structured, and their energy will be concentrated on a few principal components. This allows the change characteristics of these principal components to effectively focus on and characterize the dominant dynamic changes caused by the movement of the target object, while suppressing unstructured random noise interference and more accurately determining whether there is a moving target within the monitoring range.

[0071] Step S35: Select at least two features from the amplitude change intensity feature, the time variance feature, the time correlation feature, and the principal component change feature as the multidimensional amplitude feature.

[0072] Step S36: Weighted summation of the multidimensional amplitude features to obtain the target dynamic index.

[0073] For example, among the above-mentioned amplitude change intensity features, time variance features, time correlation features and principal component change features, at least two features are selected as multidimensional amplitude features, the weights corresponding to the two selected features are obtained, and the multidimensional amplitude features and their corresponding weights are weighted and summed to obtain the target dynamic index.

[0074] Step S40: When the numerical relationship between the target dynamic indicator and the first preset threshold does not match the current operating mode of the camera device, control the camera device to perform a mode switching action, wherein the camera device is currently in a sleep mode or a working mode.

[0075] For example, when the target dynamic index is less than a first preset threshold, it indicates that the target is stationary within the monitoring range and there are no valid moving targets. The camera should then enter sleep mode to save energy. When the target dynamic index is greater than or equal to the first preset threshold, it indicates that there is a moving target within the monitoring range. The camera should then enter working mode to detect the target object.

[0076] The aforementioned camera control system changes the normally-on operating mode of the camera. When the system determines that there is no target movement within the monitoring range based on channel status information, it enters sleep mode, thereby reducing the operating time of the high-power image analysis unit to the minimum necessary limit and achieving power saving.

[0077] Second Embodiment Based on the first embodiment described above, in the second embodiment of this application, referring to... Figure 3 The above step S40 includes steps S41 to S44: Step S41: When the target dynamic index is less than the first preset threshold, control the camera device to switch to the sleep mode.

[0078] For example, when the target dynamic index is less than the first preset threshold, the camera control unit controls the camera device to enter a sleep mode, maintaining only the normal operation of the wireless signal communication unit, the channel state acquisition unit, and the channel state analysis unit, which can significantly reduce the power consumption of the camera device.

[0079] Step S42: When the target dynamic index is greater than or equal to the first preset threshold, control the camera device to switch to the first working mode to control the camera device to acquire images and detect the images to determine whether there is a target object in the images.

[0080] For example, the camera control unit receives the target dynamic index sent by the channel state analysis unit. When the target dynamic index is greater than or equal to the second preset threshold, it is determined that there is a significant and continuous spatial disturbance within the monitoring range, and there may be a moving target. At this time, visual verification is initiated, and the camera device is controlled to enter the first working mode. The image is acquired by the image acquisition unit and the image is detected by the target tracking unit to determine whether there is a target object in the image.

[0081] After the camera device enters the first working mode, it controls the sensor of the image acquisition unit to acquire images according to the image acquisition frame rate corresponding to the first working mode. In other variations of this solution, the image acquisition unit can also be controlled to acquire images according to other parameters corresponding to the first working mode, such as exposure or supplementary light intensity.

[0082] After the image acquisition unit acquires an image, the target tracking unit performs target detection on each frame or every few frames using a pre-trained target detection algorithm and outputs the target detection confidence score. When the target detection confidence score is greater than or equal to a preset target detection confidence threshold, it is determined that a target object exists in the image. When the target detection confidence score is less than the preset target detection confidence threshold, it is determined that no target object exists in the image.

[0083] Step S43: If the target object exists in the image, control the camera device to switch to the second working mode.

[0084] It should be noted that the image acquisition frame rate corresponding to the first working mode is lower than that corresponding to the second working mode. This allows the image acquisition unit to acquire images at a higher frame rate when a target object is present in the image, enabling more accurate tracking of the target object.

[0085] Step S44: If the target object is not present in the image, control the camera device to switch to the sleep mode.

[0086] When the target object is not present in the image, in order to save energy, the camera device is controlled to enter sleep mode again, shutting down the image acquisition unit and the target tracking unit.

[0087] The solution provided in this embodiment shuts down the high-power image acquisition unit and target tracking unit during periods without a target, allowing only the low-power wireless signal communication unit, channel state acquisition unit, and channel state analysis unit to operate continuously. When a target object is detected within the monitoring range, the image acquisition unit first enters a low frame rate mode to perform necessary target detection. Only after verifying the actual presence of the target object does the camera device begin high-power image acquisition for efficient operation; otherwise, it re-enters sleep mode. This control process alters the normally on operating mode of the camera device, thereby reducing the operating time of the high-power units to the minimum necessary limit and achieving power savings.

[0088] Third Embodiment Based on the second embodiment described above, in the solution provided in this embodiment, refer to Figure 4 Following step S43, the camera device control method further includes steps S45-S50: Step S45: Determine the bounding box of the target object in the image, and use the center coordinates of the bounding box as the position coordinates of the target object.

[0089] For example, after the camera device enters the second working mode, the target tracking unit identifies the bounding box of the target object in the image using a pre-trained target detection algorithm, and outputs the center coordinates (x, y), width w, and height h of the bounding box. The center coordinates (x, y) of the bounding box are then used as the position coordinates of the target object.

[0090] For example, the target tracking unit records and maintains a target state history queue, which includes the center coordinates (x_t, y_t), width w_t, and height h_t of the bounding box corresponding to the target object in the t-th frame image, or the area area_t = w_t × h_t.

[0091] Step S46: Determine the distance between the position coordinates of the target object in adjacent image frames.

[0092] For example, the Euclidean distance or Manhattan distance between the center coordinates of the bounding boxes in adjacent image frames is calculated, and the Euclidean distance or Manhattan distance is used as the distance between the position coordinates of the target object in adjacent image frames.

[0093] Step S47: Determine the variance of the position coordinates of the target object in a preset number of image frames.

[0094] For example, the center coordinate sequence of the bounding box of the target object in N frames of images is extracted from the target state history queue, and the variance of the center coordinate sequence is calculated as the variance of the position coordinates of the target object in a preset number of image frames.

[0095] Step S48: Determine the size change of the bounding box corresponding to the target object in adjacent image frames.

[0096] For example, the rate of change of the width and height of the bounding box in adjacent image frames is calculated, or the rate of change of the area of ​​the bounding box in adjacent image frames is calculated, and the rate of change of the width and height or the rate of change of the area is used as the size change of the bounding box of the target object in the adjacent image frames.

[0097] Step S49: The distance, the variance, and the size change are weighted and summed to obtain the tracking confidence level.

[0098] For example, the weights corresponding to distance, variance, and size change are obtained, and the weights corresponding to the multidimensional amplitude features and distance, variance, and size change are weighted and summed with their corresponding weights to obtain the tracking confidence.

[0099] Optionally, in other variations of this scheme, in addition to determining the tracking confidence based on the distance, variance, and size change mentioned above, the appearance feature vector of the target object can be extracted using the target detection algorithm mentioned above, the similarity between appearance feature vectors in adjacent image frames can be calculated, and the tracking confidence can be obtained by weighted summation of the distance, variance, size change, and similarity mentioned above.

[0100] Step S50: When the tracking confidence is less than or equal to a preset confidence threshold, the tracking status of the camera device is determined based on the target dynamic index and the tracking confidence.

[0101] In the first optional scheme for determining the tracking status of the target tracking unit provided in this embodiment, when the tracking confidence level is less than or equal to a preset confidence threshold and the target dynamic index is greater than a second preset threshold, the target tracking unit of the camera device is controlled to maintain tracking. It should be noted that the second preset threshold is greater than the first preset threshold.

[0102] When the tracking confidence level calculated by the target tracking unit is less than or equal to a preset confidence threshold, it indicates that the vision-based tracking results are currently unreliable or at risk of being lost. At this point, the target dynamic index provided by the channel state analysis unit is queried. If the target dynamic index is greater than a second preset threshold, it indicates that although the visual information is unreliable, there is still significant and continuous motion disturbance within the monitoring range. Therefore, the target object is likely still within the monitoring range and is active. Thus, the target tracking unit of the camera device is controlled to maintain tracking to avoid prematurely ending target tracking due to short-term visual anomalies.

[0103] In the first optional scheme for determining the tracking state of the target tracking unit described above, optionally, after controlling the target tracking unit of the camera device to maintain tracking, the detection range and detection step size of the target tracking unit of the camera device for the image can be determined according to the target dynamic index. Here, both the detection range and the detection step size are proportional to the target dynamic index.

[0104] It should be noted that the detection range refers to the size of the spatial area that the target tracking unit needs to search within a single frame in order to find or confirm the target object tracked in the previous frame. When the tracking confidence is low but the target dynamic index is high, the target object is likely still active. Expanding the detection range can increase the probability of recapturing the target object in a wider area where it may appear, avoiding losing it due to an insufficient search area.

[0105] It should also be noted that the detection step size refers to the pixel distance that the target detection algorithm moves in the horizontal or vertical direction each time it moves its search window within the aforementioned detection range. A smaller detection step size means a denser and more refined search, but with a higher computational cost; a larger detection step size can cover the spatial area to be searched more quickly. When the target's dynamic parameters are large, increasing the detection step size can respond more quickly to possible target position jumps.

[0106] In the first optional scheme for determining the tracking state of the target tracking unit, optionally, after controlling the target tracking unit of the camera device to maintain tracking, the magnitude of the preset confidence threshold can be adjusted based on the target dynamic index. The magnitude of the preset confidence threshold is inversely proportional to the target dynamic index.

[0107] For example, when the target dynamic index is high, the preset confidence threshold is lowered. In this case, if the target object experiences brief occlusion, rapid turning, or a sudden change in lighting, causing a temporary decrease in tracking confidence, the reduced tracking confidence will still meet the conditions of the preset confidence threshold. Tracking will not be judged as a failure and terminated simply because of a brief visual fluctuation, thus maintaining the continuity of tracking and reducing the tracking loss rate.

[0108] Similarly, when the target's dynamic indicators are low, the preset confidence threshold should be increased. In this case, if the target object may have already stopped or left, but background interference or algorithm fluctuations have not caused the tracking confidence to decrease, increasing the preset confidence threshold can enable the camera device to determine that the tracking is unreliable more quickly and take actions such as termination or re-detection, thereby reducing the unnecessary consumption of computing resources when the target object may have disappeared.

[0109] In the second optional scheme for determining the tracking status of the target tracking unit provided in this embodiment, when the tracking confidence level is less than or equal to a preset confidence threshold and the target dynamic index is less than the third preset threshold, the target tracking unit of the camera device is controlled to stop tracking. It should be noted that the third preset threshold is less than the first preset threshold and less than the second preset threshold.

[0110] During target tracking, blindly persisting with tracking wastes computing power, while abandoning it too early leads to tracking interruption. The second alternative scheme for determining the tracking status of the target tracking unit mentioned above uses a third preset threshold to determine whether the target object is occluded or stationary when visual information fails. This minimizes false termination when the target object is still present but temporarily stationary or occluded, while also ensuring that invalid tracking stops promptly when the target object leaves.

[0111] In the second optional scheme for determining the tracking status of the target tracking unit, optionally, after controlling the target tracking unit of the camera device to stop tracking, the increment of the target dynamic index within a preset time period can be determined. If the increment is greater than the preset increment threshold, the target tracking unit of the camera device can be controlled to start tracking again.

[0112] To prevent the target tracking unit from failing to respond promptly when the target becomes active again or a new target appears after tracking has stopped, the increment of the target's dynamic indicators is determined within a preset time period while ensuring low power consumption. When the increment of the target's dynamic indicators within the preset time period exceeds a preset increment threshold, it indicates that a significant disturbance matching the motion characteristics has reappeared in the space. The target tracking unit of the camera device is immediately restarted to track the target, thereby enabling the target object to be quickly recaptured and tracked.

[0113] For example, to help understand the implementation flow of the camera device control method obtained by combining this embodiment with the above embodiments, please refer to... Figure 5 , Figure 5 A simplified flowchart of a camera device control method is provided, specifically: The control module first receives the wireless signal and determines the channel state information, then calculates the target dynamic index and compares it with a first preset threshold: if the target dynamic index is less than the first preset threshold, the environment is considered static, and the camera enters sleep mode to save energy; if the target dynamic index is greater than the first preset threshold, significant disturbance is detected, and the camera enters a first working mode to start image acquisition for visual confirmation. After acquiring images in the first working mode, it further determines whether a target object exists in the image: if not, the camera returns to sleep mode; if it exists, the camera enters a second working mode to start tracking the target object and calculates the tracking confidence. During tracking, if the tracking confidence is less than or equal to a preset tracking confidence threshold, the target dynamic index is used for joint decision-making: if the target dynamic index is greater than the second preset threshold, tracking continues; if the target dynamic index is less than the third preset threshold, tracking stops. After tracking stops, the target dynamic index is continuously monitored. If, within a preset time period, the increment of the target dynamic index is greater than a preset increment threshold, re-tracking is triggered, and a new round of image acquisition and target judgment begins.

[0114] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the camera device control method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0115] This application provides a camera device control device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the camera device control method in the above embodiment 1.

[0116] The following is for reference. Figure 6 The diagram illustrates a structural schematic of a camera control device suitable for implementing embodiments of this application. The camera control device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, and tablets (PADs), as well as fixed terminals such as desktop computers. Figure 6 The camera control device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0117] like Figure 6 As shown, the camera control device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the camera control device. The processing unit 1001, the ROM 1002, and the RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the camera control device to communicate wirelessly or wiredly with other devices to exchange data. Although a camera control device with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented alternatively.

[0118] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0119] The camera control device provided in this application, employing the camera control method described in the above embodiments, can solve the technical problem of how to reduce the power consumption of the camera device. Compared with the prior art, the beneficial effects of the camera control device provided in this application are the same as those of the camera control method provided in the above embodiments, and other technical features of this camera control device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0120] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0121] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0122] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the camera device control method in the above embodiments.

[0123] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, radio frequency (RF), etc., or any suitable combination thereof.

[0124] The aforementioned computer-readable storage medium may be included in the camera equipment control device; or it may exist independently and not assembled into the camera equipment control device.

[0125] The aforementioned computer-readable storage medium carries one or more programs that, when executed by a camera device control device, enable the camera device control device to write computer program code for performing the operations of this application in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, or as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0126] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0127] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0128] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described camera device control method, thereby solving the technical problem of how to reduce the power consumption of the camera device. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the camera device control method provided in the above embodiments, and will not be repeated here.

[0129] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the camera device control method described above.

[0130] The computer program product provided in this application can solve the technical problem of how to reduce the power consumption of camera equipment. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the camera equipment control method provided in the above embodiments, and will not be repeated here.

[0131] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for controlling a camera device, characterized in that, The camera equipment control method includes the following steps: Establish a wireless connection with the wireless access device and determine channel state information based on the received wireless signals; The amplitude value of each subcarrier at each sampling time is determined based on the channel state information. The multidimensional amplitude characteristics are determined based on the amplitude values, and the target dynamic indicators corresponding to the wireless channel are determined based on the multidimensional amplitude characteristics. When the numerical relationship between the target dynamic indicator and the first preset threshold does not match the current operating mode of the camera device, the camera device is controlled to perform a mode switching action, wherein the camera device is currently in a sleep mode or a working mode.

2. The camera equipment control method as described in claim 1, characterized in that, The channel state information is a three-dimensional complex tensor describing the channel coefficients corresponding to each subcarrier, each transmit antenna, and each receive antenna. The step of determining the amplitude value of each subcarrier at each sampling time based on the channel state information includes: The absolute value of each channel coefficient in the three-dimensional complex tensor is determined as the amplitude value corresponding to the channel coefficient; The amplitude values ​​of the transmitting and receiving antennas on the same subcarrier are aggregated to obtain the amplitude value of each subcarrier at each sampling time.

3. The camera equipment control method as described in claim 1, characterized in that, The multidimensional amplitude features include amplitude change intensity features, time variance features, time correlation features, and principal component change features; The steps of determining multi-dimensional amplitude features based on the amplitude value and determining the target dynamic index corresponding to the wireless channel based on the multi-dimensional amplitude features include: Within a preset time window, the mean square value of the change in amplitude value corresponding to each subcarrier is determined, and the mean square values ​​corresponding to each subcarrier are aggregated to obtain the amplitude change intensity feature; Within the preset time window, the variance of the amplitude value corresponding to each subcarrier is determined, and the variances corresponding to each subcarrier are aggregated to obtain the time variance feature; The amplitude values ​​corresponding to each subcarrier are aggregated to obtain an amplitude sequence, and the correlation coefficient between the amplitude sequences corresponding to two different preset time windows is determined, and the correlation coefficient is used as the time correlation feature. An amplitude-time matrix is ​​constructed based on the amplitude values ​​corresponding to each subcarrier. Principal component analysis is performed on the amplitude-time matrix to obtain principal component eigenvalues. The sum of the principal component eigenvalues ​​or the proportion of the sum of the principal component eigenvalues ​​to the sum of all eigenvalues ​​is used as the principal component variation feature. At least two of the following features—the amplitude variation intensity feature, the time variance feature, the time correlation feature, and the principal component variation feature—are used as the multidimensional amplitude feature; The target dynamic index is obtained by weighted summation of the multidimensional amplitude features.

4. The camera equipment control method as described in claim 1, characterized in that, The operating modes include a first operating mode and a second operating mode. The step of controlling the camera device to perform a mode switching action when the numerical relationship between the target dynamic indicator and the first preset threshold does not match the current operating mode of the camera device includes: When the target dynamic index is less than the first preset threshold, the camera device is controlled to switch to the sleep mode; When the target dynamic index is greater than or equal to the first preset threshold, the camera device is controlled to switch to the first working mode to control the camera device to acquire images and detect the images to determine whether there is a target object in the images; If the target object exists in the image, the camera device is controlled to switch to the second working mode; If the target object is not present in the image, the camera device is controlled to switch to the sleep mode, wherein the image acquisition frame rate corresponding to the first working mode is less than the image acquisition frame rate corresponding to the second working mode.

5. The camera equipment control method as described in claim 4, characterized in that, After the step of controlling the camera device to switch to the second working mode if the target object exists in the image, the method further includes: Determine the bounding box of the target object in the image, and use the center coordinates of the bounding box as the position coordinates of the target object; Determine the distance between the position coordinates of the target object in adjacent image frames; Determine the variance of the position coordinates of the target object in a preset number of image frames; Determine the size change of the bounding box corresponding to the target object in adjacent image frames; The tracking confidence score is obtained by weighted summation of the distance, the variance, and the size change. When the tracking confidence level is less than or equal to a preset confidence threshold, the tracking status of the camera device is determined based on the target dynamic index and the tracking confidence level.

6. The camera equipment control method as described in claim 5, characterized in that, The step of determining the tracking status of the camera device based on the target dynamic index and the tracking confidence level includes: When the tracking confidence level is less than or equal to the preset confidence threshold and the target dynamic index is greater than the second preset threshold, the camera device is controlled to maintain tracking. When the tracking confidence is less than or equal to the preset confidence threshold and the target dynamic index is less than the third preset threshold, the camera device is controlled to stop tracking, wherein the second preset threshold is greater than the third preset threshold and the third preset threshold is greater than the first preset threshold.

7. The camera equipment control method as described in claim 6, characterized in that, Following the step of controlling the camera device to maintain tracking, the method further includes: The detection range and detection step size of the camera device for the image are determined based on the target dynamic index, and the detection range and the detection step size are proportional to the target dynamic index. The magnitude of the preset reliability threshold is determined based on the target dynamic index, and the magnitude of the preset reliability threshold is inversely proportional to the target dynamic index.

8. The camera equipment control method as described in claim 7, characterized in that, After the step of controlling the target tracking unit to stop tracking, the method further includes: Determine the increment of the target dynamic indicator within a preset time period. If the increment is greater than a preset increment threshold, re-control the camera device to track.

9. A camera control device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the camera device control method as described in any one of claims 1 to 8.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the camera device control method as described in any one of claims 1 to 8.

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