Low-power-consumption wireless camera adaptive night vision system and energy-saving control method

Abstract

The invention provides a low-power-consumption wireless camera self-adaptive night vision system and an energy-saving control method, and relates to the technical field of image communication and monitoring, an infrared detection unit is used for identifying human infrared signals so as to judge whether video recording needs to be started or not, a partition acquisition strategy is started according to personnel position coordinates, and meanwhile, the energy-saving control method is used for monitoring the human infrared signals. By concentrating night light supplement of the wireless camera and reducing misjudgment of moving objects such as animals, the endurance of the wireless camera is improved, and meanwhile useful video data are collected in a targeted mode, so that loss of personnel and property under the condition similar to camping is reduced.

Description

Technical Field

[0001] The present invention relates to the field of image communication and monitoring technology, and more specifically, to a low-power wireless camera adaptive night vision system and an energy-saving control method. Background Art

[0002] In recent years, with the development of smart home and intelligent security technologies, wireless cameras, as the core monitoring devices in home, commercial and industrial scenarios, have been widely used. The functions of wireless cameras have gradually shifted from simple video acquisition and transmission to the direction of intelligence and energy conservation. In traditional monitoring systems, cameras are usually in a continuous running state to ensure real-time monitoring. However, this 24-hour uninterrupted working mode often leads to excessive power consumption of the device, especially in scenarios where there is no human activity, and the waste of resources is particularly serious. In addition, with the increasing demand for night monitoring, traditional cameras generally rely on continuously turned-on infrared fill lights to obtain night vision images, which not only further increases power consumption but also may cause light pollution problems. In recent years, the introduction of low-power technologies such as human thermal infrared sensors and environmental light sensors has provided new possibilities for the energy-saving optimization of wireless cameras, but there are still many limitations in the device wake-up response speed, energy consumption management, and the clarity and range control of night vision images in the existing technologies.

[0003] In the existing technologies, although some wireless cameras have integrated thermal infrared sensors to detect human activities and achieve wake-up control of the cameras, their detection accuracy and response speed are limited by the signal processing capabilities of the sensors, and it is difficult to accurately locate the human position in complex scenarios. In addition, most existing night vision cameras usually adopt a fixed-power infrared fill light method when the light is insufficient, lacking the dynamic adaptability to environmental light conditions, which easily leads to problems such as excessive energy consumption or insufficient fill light. In terms of image acquisition, traditional cameras usually use a single frame rate to collect the entire scene and cannot perform differential processing according to the importance of the monitored area, resulting in data redundancy and low processing efficiency. More importantly, the existing technologies have limited processing capabilities for multi-target detection. Especially in scenarios with multiple human activities, the field of view coverage and tracking capabilities of the cameras are difficult to meet the monitoring requirements. The existence of the above problems greatly restricts the further development of wireless cameras in terms of energy conservation, intelligence and adaptability. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a low-power wireless camera adaptive night vision system and an energy-saving control method, which can, to a certain extent, solve problems such as the inability to monitor the whole night due to the need for long-term fill light in outdoor camping situations, resulting in the loss of camping equipment.

[0005] According to one aspect of the present invention, there is provided a low-power wireless camera adaptive night vision system, which includes:

[0006] A thermal infrared sensor array, including two independent infrared sensor units, for detecting human heat sources;

[0007] A signal processing unit, for converting the infrared signals detected by the infrared sensor units into digital trigger signals, and calculating the position target of the human heat source by using a triangulation algorithm;

[0008] A control unit, for adjusting the imaging angle of a wireless camera according to the position target obtained by the signal processing unit, and detecting the ambient light intensity for supplementary lighting; when no human heat source is detected within a preset continuous time, controlling the camera to enter a sleep mode;

[0009] An image acquisition unit, for adaptively acquiring high-definition image data of key areas according to a dual-frame rate acquisition strategy.

[0010] Further, the triangulation algorithm calculates the distance and angle of the heat source relative to the sensor through the difference and ratio of the heat source signal intensities received by the infrared sensor units, in combination with the installation angle and the piecewise linear fitting method, and finally determines the position coordinates of the heat source.

[0011] Further, when the infrared sensor array detects multiple heat source signals at the same time, the targets are separated based on the time-domain waveform characteristics of the heat source signals, independent heat sources are identified by filtering non-human characteristic frequencies and detecting waveform peaks, and the position coordinates of each heat source are calculated in the order of signal intensity. If the angular interval between adjacent heat sources is less than a preset value, they are merged into one target for tracking.

[0012] Further, adjusting the imaging angle of the wireless camera according to the position coordinates includes:

[0013] Judging whether the target is within the effective field of view. When the target is within the effective field of view, calculating the horizontal deflection angle θ of the line connecting the target position and the camera center relative to the 0-degree reference line;

[0014] At the same time, calculating the required pitch angle φ of the camera;

[0015] Based on the calculated θ value, controlling the horizontal rotation motor of the built-in camera component to rotate by the corresponding angle;

[0016] Based on the φ value, controlling the pitch motor to adjust the angle.

[0017] Further, calculating the horizontal deflection angle includes:

[0018] Setting the installation position of the camera as the coordinate origin;

[0019] After obtaining the target position coordinates C(x, y), calculate the horizontal deflection angle θ through the arctangent function;

[0020] Calculate the horizontal deflection angle based on the quadrant position of the target position coordinates.

[0021] Further, adjusting the shooting angle of the wireless camera according to the position coordinates further includes, when detecting multiple target position coordinates, adopting an optimal field of view coverage strategy for angle adjustment.

[0022] Further, the dual-frame rate acquisition strategy adopts different acquisition frequencies for the core area and the surrounding area;

[0023] Determine the core area, calculate the reference size of the core area based on the target distance information; then use the target position coordinates C(x, y) as the center point of the core area, and expand equally in all directions to form a rectangular area; among them, the expansion range is determined by the reference size of the core area.

[0024] Further, calculating the reference size of the core area includes:

[0025] Obtain the actual distance value of the target through a depth sensor or a binocular ranging module, and at the same time obtain the pixel size of the target in the image;

[0026] Calculate the reference size according to the target distance by establishing a piecewise linear mapping function;

[0027] If there are multiple targets at the same time, the system takes the maximum value of the calculation results of each target as the final reference size.

[0028] Further, when the infrared sensor unit does not detect a human heat source signal within a preset time, switch the wireless camera to the sleep state, and only keep the human thermal infrared sensor working.

[0029] According to another aspect of the present invention, there is provided a low-power wireless camera energy-saving control method, which includes:

[0030] Detect the human heat source signal in the surrounding area through the human thermal infrared sensor of the wireless camera. When detecting the human heat source signal, trigger the wireless camera to switch from the sleep state to the working state, and record the position coordinates of the human heat source signal;

[0031] Adjust the shooting angle of the wireless camera according to the position coordinates, and at the same time collect ambient light intensity data based on the built-in photosensitive sensor of the wireless camera. When the ambient light intensity data is lower than the preset light threshold, start the micro-power infrared fill light array;

[0032] Adopt a dual-frame rate acquisition strategy to acquire high-definition image data of the area corresponding to the position coordinates at a high frame rate, and at the same time acquire image data of the surrounding area at a low frame rate;

[0033] When the human heat source signal is not detected within the preset time, the wireless camera automatically switches to the sleep state, and only keeps the human thermal infrared sensor working.

[0034] Compared with the prior art, a low-power wireless camera adaptive night vision system and an energy-saving control method provided by the present invention identify the human infrared signal through the infrared detection unit, and then judge whether to start video recording, and enable a partition acquisition strategy for the personnel position coordinates. In this way, by concentrating the night-time supplementary lighting of the wireless camera and reducing the misjudgment of moving objects such as animals, while improving the battery life of the wireless camera, targeted acquisition of useful video data is carried out, thereby reducing the loss of personnel property in situations such as camping. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0036] Figure 1 It is a system block diagram of a low-power wireless camera adaptive night vision system according to an embodiment of the present invention.

[0037] Figure 2 It is a flowchart of an energy-saving control method for a low-power wireless camera according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited by the exemplary embodiments described herein.

[0039] Figure 1 It is a system block diagram of a low-power wireless camera adaptive night vision system according to an embodiment of the present invention. As Figure 1 shown, in the low-power wireless camera adaptive night vision system, it includes:

[0040] A thermal infrared sensor array, including two independent infrared sensor units, for detecting the human heat source;

[0041] A signal processing unit, configured to convert the infrared signals detected by the infrared sensor unit into digital trigger signals, and calculate the position target of the human heat source by using a triangulation algorithm;

[0042] A control unit, configured to adjust the imaging angle of the wireless camera according to the position target obtained by the signal processing unit, detect the ambient light intensity, and perform light compensation; when the human heat source is not detected within a preset continuous time, control the camera to enter a sleep mode;

[0043] An image acquisition unit, configured to adaptively acquire high-definition image data of a key area according to a dual frame rate acquisition strategy.

[0044] The wireless camera adopts a dual human body thermal infrared sensor array with a 180-degree fan-shaped coverage. The dual human body thermal infrared sensor array includes 2 independent thermal infrared sensor units, and the detection angle of each thermal infrared sensor unit is 90 degrees, so as to form a continuous 180-degree fan-shaped heat source detection area on the horizontal plane through the dual human body thermal infrared sensor array; wherein, each thermal infrared sensor unit adopts a low-power analog signal processing circuit, and the low-power analog signal processing circuit includes a signal amplifier, a band-pass filter and a comparator, and is configured to convert the weak infrared signals of the human heat source into digital trigger signals; when any one of the thermal infrared sensor units detects a human heat source, the corresponding digital trigger signal is processed by a microcontroller and then controls the wireless camera to switch from a sleep state to a working state, and the response time of the switching process does not exceed 100 milliseconds; meanwhile, based on the signal intensity difference of the dual human body thermal infrared sensor array and in combination with the known installation angles of the two thermal infrared sensor units, the position coordinates of the human heat source signal are calculated by using a triangulation algorithm, and the position coordinates include a distance value and an angle value relative to the wireless camera.

[0045] The described triangulation algorithm is calculated based on the heat source signal intensity received by the dual thermal infrared sensor unit. When the dual thermal infrared sensor unit simultaneously detects the human heat source signal, the heat source signal intensity values received by the two sensor units are respectively obtained, where the signal intensity value has an inverse square relationship with the heat source distance; through the known installation angle and signal intensity difference of the dual thermal infrared sensor unit, a mathematical relationship model between the heat source position and the signal intensity is established. When the signal intensity of the left sensor unit is greater than that of the right sensor unit, it indicates that the heat source position is biased to the left, and vice versa, it is biased to the right; if the signal intensity difference between the two sensor units is less than the preset threshold, it indicates that the heat source is on the vertical direction of the center line connecting the two sensor units; the angle between the heat source and the two sensor units is calculated based on the ratio of the signal intensity, and at the same time, the heat source distance is estimated using the absolute value of the signal intensity. The distance estimation adopts a piecewise linear fitting method, dividing the detection range of 0 - 5 meters into three intervals: near, middle, and far, and different linear coefficients are used for each interval. Finally, the distance value and angle value of the human heat source signal relative to the wireless camera are obtained, forming the position coordinates of the heat source. Specifically, the obtained position coordinates of the heat source can be expressed by the following formula:

[0046]

[0047] Where:

[0048]

[0049] Among them, C(x, y) is the two-dimensional coordinate of the heat source target, d is the estimated target distance (unit: meter), α is the deflection angle of the target relative to the camera center line (unit: radian), S l is the signal intensity value received by the left sensor (unit: μW / cm 2 ), S r is the signal intensity value received by the right sensor (unit: μW / cm 2 ), λ is the sensitivity coefficient of the signal intensity difference (value 0.8), β is the distance correction coefficient (value 0.5), ∈ is the threshold for judging whether the target is on the center line (value 0.1 μW / cm 2 ), k1, k2, k3 are the linear coefficients of the near, middle, and far distance intervals respectively (values 2.5, 3.2, 4.0 respectively), and θ is the detection angle of a single sensor (value 45°).

[0050] More specifically, when the dual thermal infrared sensor unit detects multiple heat source signals at the same time, target separation is first performed based on the time-domain waveform characteristics of the heat source signals; specifically, the human body heat source signal has a characteristic frequency fluctuation of 0.8 - 1.2 Hz, while other heat sources usually do not have this frequency characteristic, and accordingly, non-human heat source interference is filtered out; for multiple human body heat source signals, each independent heat source is identified by the peak detection method of the signal waveform, where the minimum resolvable angle between adjacent heat sources is 15 degrees; when multiple independent heat sources are detected, the triangulation algorithm calculates the position coordinates of each heat source in order of signal strength from largest to smallest; if the angular interval between two heat sources is less than 15 degrees, they are combined into one target for tracking, and further, the above method for calculating the position coordinates is used to obtain the position coordinates of each heat source.

[0051] On the other hand, the wireless camera adopts a fixed housing design. The front of the housing is a 180-degree fan-shaped transparent protective cover, and a rotatable camera assembly is installed inside; after obtaining the target position coordinates C(x, y), it is first determined whether the target is within the 180-degree fan-shaped field of view, where the camera center is the origin, the due front is the 0-degree reference line, and the effective field of view is composed of 90 degrees to the left and right; when the target is within the effective field of view, the horizontal deflection angle θ of the line connecting the target position and the camera center relative to the 0-degree reference line is calculated, where a left deflection is a positive value and a right deflection is a negative value, and the value range of θ is limited to between -90 degrees and +90 degrees; at the same time, the required pitch angle φ of the camera is calculated, and φ is equal to the inclination angle of the target position and the camera horizontal plane, with an upward inclination being a positive value and a downward inclination being a negative value; based on the calculated θ value, the horizontal rotation motor of the built-in camera assembly is controlled to rotate at the corresponding angle, where a dynamic acceleration strategy is adopted: when |θ| is greater than 45 degrees, the motor rotates at a speed of 12 degrees per second, when |θ| is between 15 degrees and 45 degrees, the motor rotates at a speed of 8 degrees per second, and when |θ| is less than 15 degrees, the motor rotates at a speed of 4 degrees per second to ensure fast adjustment at large angles and precise tracking at small angles; similarly, based on the φ value, the pitch motor is controlled to adjust the angle, and the pitch angle range is limited to between 30 degrees and 80 degrees; during the motor rotation, the system updates the target position coordinates every 100 milliseconds, and when the deviation between the angle calculated from the new coordinates and the current angle exceeds 3 degrees, angle correction is immediately started to achieve smooth tracking.

[0052] More specifically, the calculation of the horizontal deflection angle is as follows:

[0053] First, set the installation position of the camera as the coordinate origin O(0, 0), the positive front reference line as the positive x-axis direction, and the direction perpendicular to the ground upward as the positive y-axis direction; after obtaining the target position coordinates C(x, y), calculate the horizontal deflection angle θ through the arctangent function, where θ = arctan(y / x); if the target is in the first quadrant (x > 0, y > 0), the θ value directly adopts the calculation result of the arctangent function; if the target is in the second quadrant (x < 0, y > 0), the θ value is equal to the calculation result of the arctangent function plus 180 degrees; if the target is in the third quadrant (x < 0, y < 0), the θ value is equal to the calculation result of the arctangent function plus 180 degrees; if the target is in the fourth quadrant (x > 0, y < 0), the θ value is equal to the calculation result of the arctangent function; when x is equal to 0, if y is greater than 0, then θ is equal to 90 degrees, if y is less than 0, then θ is equal to -90 degrees; when y is equal to 0, if x is greater than 0, then θ is equal to 0 degrees, if x is less than 0, then θ is equal to 180 degrees; if the calculated θ value is greater than 90 degrees or less than -90 degrees, it indicates that the target is behind the camera and exceeds the 180-degree fan-shaped field of view range. At this time, the system will abandon tracking and record the out-of-bounds status; finally, perform precision correction on the θ value within the effective range, retain the calculation result to one decimal place, and eliminate the cumulative error in the calculation process.

[0054] When the wireless camera detects multiple target position coordinates, it adopts the optimal field of view coverage strategy for angle adjustment; when multiple targets are detected simultaneously, first calculate the distribution range of all target position coordinates on the horizontal plane, and obtain the angle θL of the leftmost target and the angle θR of the rightmost target; if the distribution range of all targets (|θR - θL|) is less than 90 degrees, adjust the camera to the center position of the angle distribution, that is, θ = (θR + θL) / 2, so that all targets are within the effective field of view of the camera; if the target distribution range is greater than 90 degrees, the system calculates the 90-degree sector with the highest target density and aligns the camera with the center position of this sector to ensure that the most targets are covered within the limited field of view; at the same time, calculate the distribution range of the pitch angles of all targets, obtain the highest target pitch angle φH and the lowest target pitch angle φL, and adjust the pitch angle of the camera to φ = (φH + φL) / 2 to achieve the best coverage in the vertical direction; the system updates the target distribution calculation every 200 milliseconds. When the target distribution range changes significantly (greater than 10 degrees), recalculate the optimal coverage angle and make adjustments. The angle adjustment adopts uniform motion, and the rotation speed is controlled within 8 degrees per second to ensure smooth transition of the picture.

[0055] When the wireless camera detects that the distribution range of multiple targets exceeds 90 degrees, it uses the sliding sector scanning method to calculate the target density. First, all targets are sorted in ascending order of the horizontal angle θ to obtain the target sequence T(θ1, θ2,..., θn). Starting from the leftmost target θ1, a scanning sector with a width of 90 degrees is constructed, and the number of targets N1 falling within this sector is counted. Then, the scanning sector is slid to the right with a sliding step of 5 degrees, and the number of targets within the sector is re-counted at each step position. When the right boundary of the scanning sector reaches the rightmost target θn, a complete scan is completed. The system records the number of targets at each position during the scan. When the number of targets in multiple sectors is the same, the sector with the smallest average angular interval between targets is selected as the optimal sector. If the targets are unevenly distributed in a certain sector, the system calculates the local density of the targets, where the local density is defined as the reciprocal of the angular interval between adjacent targets, and the total density of the sector is the weighted sum of all local densities. Finally, the 90-degree sector with the highest total density is selected, and the camera is aligned with the center position of this sector.

[0056] The image acquisition unit first determines the key monitoring area based on the obtained position coordinates, and divides the image acquisition area into a core area and a peripheral area. Among them, the core area forms a rectangular area by expanding a preset pixel range around the target position coordinates in all directions, and the size of this area is adaptively adjusted according to the target distance. The closer the distance, the larger the core area. After the core area is determined, the system starts the dual-frame rate acquisition mode, uses a high frame rate to acquire the core area to ensure the continuity of motion details, and at the same time uses a low frame rate to acquire the peripheral area outside the core area for scene situation awareness. During the image acquisition process, the system acquires and caches each frame of image data in the core area at a high definition resolution of 1920×1080, while the peripheral area uses a standard resolution of 1280×720. When the system completes the acquisition of one frame of image, it immediately starts the face detection algorithm to perform real-time analysis on the high definition image of the core area. If a face area is detected, this area is extracted and saved separately. The extraction of the face area adopts an adaptive expansion strategy, that is, on the basis of the detected face frame, an area 20% larger is expanded outward to ensure the complete preservation of face features. The extracted face image data is stored in a lossless compression manner, and the file name contains timestamp and position coordinate information for subsequent retrieval. If the target position changes, the system updates the position of the core area in real time to ensure that the high frame rate acquisition always follows the target movement.

[0057] The system realizes dual-frame rate acquisition control based on a timing controller. First, a global clock reference is established, generating 24 acquisition trigger signals per second. When a trigger signal is generated, the system determines the current trigger sequence number, takes the remainder of the trigger signal sequence number divided by 4. When the remainder is 0, it simultaneously triggers the image acquisition of the core area and the peripheral area. When the remainder is not 0, it only triggers the image acquisition of the core area. After receiving the acquisition trigger signal, the system first reads the current position coordinates of the target to determine the position and size of the core area, and then starts the local exposure control of the image sensor, setting exposure parameters for the core area and the peripheral area respectively. When performing core area acquisition, the system limits the photosensitive area of the image sensor within the core area range and completes image acquisition in full resolution mode. When it is necessary to simultaneously acquire the peripheral area, the system first completes the acquisition of the core area, and then immediately switches to the acquisition parameters of the peripheral area to complete the image acquisition of the remaining area. If the change in the target position causes the change of the core area position, the system updates the division of the acquisition area when the next trigger cycle arrives. When the system detects that the acquisition delay exceeds the preset threshold, it automatically skips one acquisition of the peripheral area to ensure the stability of the acquisition frequency of the core area. When stitching the dual-region images, the system performs motion compensation based on the time difference between the latest acquisitions of the two regions to eliminate the image discontinuity caused by the frame rate difference.

[0058] Among them, the logic for determining the position and size of the core area is as follows:

[0059] The system first obtains the position coordinates c(x,y) of the target on the image plane, which represent the pixel position of the target center point in the entire image. After obtaining the target position coordinates, the system calculates the reference size of the core area based on the target distance information, where the target distance is obtained through a depth sensor or binocular ranging. The system divides the target distance into three levels: short distance, medium distance, and long distance, and sets corresponding reference sizes of the core area for each distance level. The closer the distance, the larger the reference size, and vice versa. After determining the reference size, the system uses the target position coordinates c(x,y) as the center point of the core area and expands it proportionally in all directions to form a rectangular area. The expansion range is determined by the reference size. When the calculated core area exceeds the image boundary, the system automatically performs boundary constraint processing, cropping or translating the exceeded part to ensure that the core area completely falls within the valid image range. If it is detected that the target is in a moving state, the system reserves a motion margin according to the target motion speed and direction, and appropriately expands the core area in the motion direction. The expansion ratio is proportional to the target motion speed to avoid the target exceeding the core area when moving fast. When there are multiple targets within the same core area, the system calculates the actual size of the core area with the outermost target as the boundary to ensure that all targets are included. Each time the system determines the core area, it records the area coordinate information in the cache for pre-judgment of the next frame of image.

[0060] Among them, calculating the reference size of the core area based on the target distance information includes:

[0061] The system first obtains the actual distance value of the target through a depth sensor or a binocular ranging module, and at the same time obtains the pixel size of the target in the image; after obtaining the distance information, the system establishes a mapping relationship between the distance and the pixel size based on the actual height of the target and the imaging principle. The system establishes a piecewise linear mapping function according to the target distance, divides the distance into a near-distance segment, a mid-distance segment, and a far-distance segment, and each distance segment corresponds to a reference size calculation formula; when the target is in the near-distance segment, the ratio of the reference size of the core area to the total image size is relatively large to ensure sufficient information about the environment around the target is captured; when the target is in the mid-distance segment, the system dynamically adjusts the reference size according to the change in distance, and the adjustment curve uses a smooth transition function to avoid sudden changes in size; when the target enters the far-distance segment, the reduction rate of the reference size slows down to ensure that distant targets can still obtain sufficient image information; if there are multiple targets at the same time, the system takes the maximum value of the calculation results of each target as the final reference size. Among them, the piecewise linear mapping function is shown as follows:

[0062]

[0063] Among them, B is the output value of the reference size of the core area, d is the current distance of the target, H t is the actual height of the target, d1 is the boundary distance value between the near-distance segment and the mid-distance segment, d2 is the boundary distance value between the mid-distance segment and the far-distance segment, λ is the distance attenuation factor, S min is the minimum threshold of the reference size, and tanh is the hyperbolic tangent function used to achieve smooth transition.

[0064] More specifically, the optimal λ value and d1, d2 values can be calculated through the following formula:

[0065]

[0066] Among them, λ opt is the optimal attenuation factor, R FOV is the effective field of view range of the camera, θ λ is the field of view angle of the camera, H t is the actual height of the target, P d is the desired target pixel size, P t is the total pixel size of the image, R max is the maximum recognition distance required by the system, R min is the minimum recognition distance required by the system.

[0067] The control unit continuously monitors the change of heat source signals in the environment through a human body thermal infrared sensor; when the sensor does not receive heat source signals conforming to human characteristics within the set time, the system starts the sleep preprocessing program; when entering the preprocessing stage, the system first stores the currently processed data completely to ensure that important information will not be lost; if there are unfinished data transmission tasks, the system will wait for the transmission to complete before entering the sleep process; after data protection is completed, the system shuts down the power supply of peripheral devices and functional modules step by step; the system first turns off the power supply of the image sensor and stops the work of the image processing unit at the same time; then it turns off the transmitting unit of the wireless communication module and only retains the necessary wake-up receiving circuit; subsequently, it reduces the working frequency of the main processor and switches to the low-power working mode; the system copies the key data in the memory to the power-off protection area to prevent data loss during sleep; after the main components enter the low-power state, the system cuts off the unnecessary power management unit and only keeps the power supply line of the thermal infrared sensor; before entering the deep sleep, the system configures the wake-up conditions and threshold parameters to ensure that it can quickly resume work when detecting a human heat source.

[0068] It should be noted that, for example, in the actual application of a mountain camping base, the wireless camera is in the deep sleep state, and only the thermal infrared sensor works. When there is a thief, the system first captures a slowly moving human target through the heat source signal. When the heat source signal intensity exceeds the preset threshold and lasts for more than five seconds, the system immediately wakes up from the sleep state. During the wake-up process, the system preferentially restores the working states of the image sensor and the processing unit, and at the same time quickly adjusts the night vision parameters: first, it adjusts the infrared fill light intensity to the most suitable level for the current environment to ensure sufficient illumination without alarming the target; then it optimizes the exposure parameters of the image sensor, adjusts the shutter speed and sensitivity to the best balance point. While collecting information, it transmits the video to the cloud in real time through wireless transmission and saves it. Even if the thief discovers the camera and damages it, the owner can still find the thief through the cloud video and recover the loss of damaged or lost camping equipment.

[0069] In summary, the low-power wireless camera adaptive night vision system based on the embodiments of the present invention is clarified. It identifies the human infrared signal through the infrared detection unit, and then judges whether to start video recording, and enables the zone acquisition strategy for the personnel position coordinates. In this way, by centralizing the night-time fill light of the wireless camera and reducing the misjudgment of moving objects such as animals, while improving the battery life of the wireless camera, it specifically collects useful video data, thereby reducing the loss of personnel property in situations such as camping.

[0070] Here, those skilled in the art can understand that the specific operations of each step in the above low-power wireless camera energy-saving control method have been referred to above Figure 1It has been introduced in detail in the description of the low-power wireless camera adaptive night vision system, and therefore, its repeated description will be omitted.

[0071] As Figure 2 shown, the energy-saving control method for a low-power wireless camera includes:

[0072] Detect the human heat source signal in the surrounding area through the human body thermal infrared sensor of the wireless camera. When the human heat source signal is detected, trigger the wireless camera to switch from the sleep state to the working state, and record the position coordinates of the human heat source signal;

[0073] Adjust the shooting angle of the wireless camera according to the position coordinates, and at the same time collect the ambient light intensity data based on the photosensitive sensor built in the wireless camera. When the ambient light intensity data is lower than the preset light threshold, start the micro-power infrared fill light array;

[0074] Adopt a dual-frame rate acquisition strategy to acquire high-definition image data of the area corresponding to the position coordinates at a high frame rate, and at the same time acquire image data of the surrounding area at a low frame rate;

[0075] When the human heat source signal is not detected within the preset time, the wireless camera automatically switches to the sleep state, and only keeps the human body thermal infrared sensor working.

[0076] In summary, as Figure 2 shown, the energy-saving control method for a low-power wireless camera based on the embodiments of the present invention is clarified. It identifies the human infrared signal through the infrared detection unit, and then judges whether to start recording, and enables a partition acquisition strategy for the personnel position coordinates. In this way, by concentrating the night-time fill light of the wireless camera and reducing the misjudgment of moving objects such as animals, while improving the battery life of the wireless camera, targeted acquisition of useful video data is carried out, thereby reducing the loss of personnel and property in situations such as camping.

Claims

1. A low-power wireless camera adaptive night vision system, characterized in that, Comprising: A thermal infrared sensor array, including two independent infrared sensor units for detecting human heat sources; A signal processing unit for converting the infrared signals detected by the infrared sensor units into digital trigger signals and calculating the position target of the human heat source using a triangulation algorithm; A control unit for adjusting the shooting angle of a wireless camera according to the position target obtained by the signal processing unit, and detecting the ambient light intensity for supplementary lighting; When no human heat source is detected within a continuous preset time, controlling the camera to enter a sleep mode; An image acquisition unit for adaptively acquiring high-definition image data of key areas according to a dual-frame rate acquisition strategy.

2. The low-power wireless camera adaptive night vision system according to claim 1, characterized in that, The triangulation algorithm calculates the distance and angle of the heat source relative to the sensor through the difference and ratio of the heat source signal intensities received by the infrared sensor units, combined with the installation angle and a piecewise linear fitting method, and finally determines the position coordinates of the heat source.

3. The low-power wireless camera adaptive night vision system according to claim 2, wherein When the infrared sensor array detects multiple heat source signals at the same time, the targets are separated based on the time-domain waveform characteristics of the heat source signals. Independent heat sources are identified by filtering non-human characteristic frequencies and detecting waveform peaks, and the position coordinates of each heat source are calculated in the order of signal intensity. If the angular interval between adjacent heat sources is less than a preset value, they are merged into one target for tracking.

4. The low-power wireless camera adaptive night vision system according to claim 1, characterized in that, Adjusting the shooting angle of the wireless camera according to the position coordinates includes: Judging whether the target is within the effective field of view. When the target is within the effective field of view, calculating the horizontal deflection angle θ of the line connecting the target position and the camera center relative to the 0-degree reference line; At the same time, calculating the required pitch angle φ of the camera; Based on the calculated θ value, controlling the horizontal rotation motor of the built-in camera component to rotate by the corresponding angle; Based on the φ value, controlling the pitch motor to adjust the angle.

5. The low-power wireless camera adaptive night vision system according to claim 4, characterized in that, Calculating the horizontal deflection angle includes: Setting the installation position of the camera as the coordinate origin; After obtaining the target position coordinates C(x, y), calculating the horizontal deflection angle θ through the arctangent function; Calculating the horizontal deflection angle through the quadrant position of the target position coordinates.

6. The low-power wireless camera adaptive night vision system according to claim 4, characterized in that Adjusting the shooting angle of the wireless camera according to the position coordinates further includes, when multiple target position coordinates are detected, adopting an optimal field of view coverage strategy for angle adjustment.

7. The low-power wireless camera adaptive night vision system according to claim 6, characterized in that, The dual-frame rate acquisition strategy uses different acquisition frequencies for the core area and the peripheral area; Determining the core area, calculating the reference size of the core area based on the target distance information; then using the target position coordinates C(x, y) as the center point of the core area, and expanding it equally in all directions to form a rectangular area; where the expansion range is determined by the reference size of the core area.

8. The low-power wireless camera adaptive night vision system according to claim 7, characterized in that Calculating the reference size of the core area includes: Obtaining the actual distance value of the target through a depth sensor or a binocular ranging module, and at the same time obtaining the pixel size of the target in the image; Calculating the reference size according to the target distance by establishing a piecewise linear mapping function; If there are multiple targets at the same time, the system takes the maximum value of the calculation results of each target as the final reference size.

9. The low-power wireless camera adaptive night vision system according to claim 1, characterized in that When the human body heat source signal is not detected by the infrared sensor unit within the preset time, the wireless camera is switched to the sleep state, and only the human body thermal infrared sensor is kept working.

10. A low-power wireless camera energy-saving control method, based on the low-power wireless camera adaptive night vision system according to any one of claims 1 to 9, characterized in that the human body thermal infrared sensor of the wireless camera is used to detect the human body heat source signal in the surrounding area. When the human body heat source signal is detected, the wireless camera is triggered to switch from the sleep state to the working state, and the position coordinates of the human body heat source signal are recorded; the shooting angle of the wireless camera is adjusted according to the position coordinates, and at the same time, the ambient light intensity data is collected based on the photosensitive sensor built in the wireless camera. When the ambient light intensity data is lower than the preset light threshold, the micro-power infrared fill light array is started; a dual-frame rate acquisition strategy is adopted to acquire high-definition image data of the area corresponding to the position coordinates at a high frame rate, and at the same time, image data of the surrounding area is acquired at a low frame rate; when the human body heat source signal is not detected within the preset time, the wireless camera automatically switches to the sleep state, and only the human body thermal infrared sensor is kept working.

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