Dynamic signal detection method and system

Abstract

A dynamic signal detection method and system, which relate to the technical field of camera detection. The method comprises: on the basis of a specified signal acquisition sensitivity, acquiring an infrared light signal in a specified direction, so as to obtain infrared detection information (S1); on the basis of the infrared detection information, determining whether there is a concealed camera in the specified direction (S2); if the determination result indicates that there is a concealed camera in the specified direction, analyzing the infrared signal intensity of the camera, and on the basis of the infrared signal intensity, adaptively adjusting the signal acquisition sensitivity (S3); and if the determination result indicates that a concealed camera cannot be detected under the current sensitivity, performing camera concealment probability analysis on the infrared detection information, and adaptively adjusting the signal acquisition sensitivity on the basis of the analysis result, so as to perform subsequent infrared detection (S4).

Description

A dynamic signal detection method and system Technical Field

[0001] This invention relates to the field of camera detection technology, and more specifically, to a dynamic signal detection method and system. Background Technology

[0002] In private spaces such as homes, dressing rooms, and restrooms, people have a stronger need for privacy protection. However, in recent years, incidents of hidden camera voyeurism have occurred frequently, causing great distress and harm to victims. This dynamic signal detection method can help people independently detect the presence of hidden cameras in their private spaces, enhancing their ability to protect their personal privacy. Traditional signal detection methods typically use fixed signal acquisition sensitivity. When encountering situations with significant changes in signal strength, detectors with fixed sensitivity may fail to accurately detect signals. For example, if the sensitivity is set too high, some interference signals in the environment may be misinterpreted as infrared signals from a camera; if the sensitivity is set too low, it may fail to detect hidden cameras with weak signals. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a dynamic signal detection method and system to accurately identify hidden cameras.

[0004] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0005] According to one aspect of the present invention, a dynamic signal detection method is provided, comprising:

[0006] Infrared light signals in a specified direction are collected based on a specified signal acquisition sensitivity to obtain infrared detection information;

[0007] Based on the infrared detection information, determine whether a hidden camera exists in the specified direction;

[0008] If the determination result is that it exists, then the infrared signal strength of the camera is analyzed, and the signal acquisition sensitivity is adaptively adjusted according to the infrared signal strength;

[0009] If the determination result is that the current sensitivity cannot detect the hidden camera, then the probability of the camera being hidden is analyzed based on the infrared detection information, and the signal acquisition sensitivity is adaptively adjusted based on the analysis result for subsequent infrared detection.

[0010] According to another aspect of the invention, a dynamic signal detection system is provided for implementing a dynamic signal detection method according to any one of the first aspects.

[0011] As can be seen from the above technical solution, the dynamic signal detection method provided by the present invention has the following beneficial effects:

[0012] This invention acquires infrared signals by specifying a sensitivity, which can initially obtain detection information. Based on this information, it can determine whether a hidden camera exists, enabling targeted detection. If a camera is found, the signal strength is analyzed and the sensitivity is adjusted to accurately capture the camera signal. If the camera is not found, the probability of its hiding is analyzed and the sensitivity is adjusted again to increase the chance of discovering the hidden camera in subsequent detections. The entire process dynamically adjusts the sensitivity to adapt to different situations, enhancing the accuracy and comprehensiveness of detection and effectively addressing the challenge of hidden camera detection. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort:

[0014] Figure 1 is a schematic diagram of the steps of a dynamic signal detection method provided in an embodiment of the present invention. Embodiments of the present invention

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] In private spaces such as homes, dressing rooms, and restrooms, people have a stronger need for privacy protection. However, in recent years, incidents of hidden camera voyeurism have occurred frequently, causing great distress and harm to victims. This dynamic signal detection method can help people independently detect the presence of hidden cameras in their private spaces, enhancing their ability to protect their personal privacy. Traditional signal detection methods typically use fixed signal acquisition sensitivity. When encountering situations with significant changes in signal strength, detectors with fixed sensitivity may fail to accurately detect the signal. For example, if the sensitivity is set too high, some interference signals in the environment may be misinterpreted as infrared signals from a camera; if the sensitivity is set too low, it may fail to detect hidden cameras with weak signals.

[0017] In view of this, the present invention provides a dynamic signal detection method, the steps of which are shown in Figure 1, including:

[0018] The first step is to collect infrared light signals in a specified direction based on the specified signal acquisition sensitivity to obtain infrared detection information;

[0019] The second step is to determine whether a hidden camera exists in the specified direction based on the infrared detection information.

[0020] The third step is to analyze the infrared signal strength of the camera and make adaptive adjustments to the signal acquisition sensitivity based on the infrared signal strength if the result is positive.

[0021] Fourth, if the determination result is that the current sensitivity cannot detect the hidden camera, the infrared detection information is analyzed to determine the probability of camera concealment, and the signal acquisition sensitivity is adaptively adjusted based on the analysis results for subsequent infrared detection.

[0022] Specifically, in the first step of the embodiment provided by the present invention, the user can hold a signal detection device and point it in the direction he / she wants to detect. This gives the user a lot of autonomy. The user can flexibly choose the area where he / she needs to detect the presence of hidden cameras according to the actual scene and his / her own needs. For example, in a hotel room, the user can detect the area around the bed, the corner of the bathroom, and other places where hidden devices are suspected to exist.

[0023] More specifically, signal detection equipment can also autonomously determine the detection direction. The specific operation involves first performing scene recognition on the optical visual data of the specified direction, dividing the specified direction into several sub-regions based on the scene recognition results, configuring corresponding detection weights for each sub-region, and then performing detection value analysis on each neighboring direction of the specified direction based on the detection weights of each sub-region, thereby determining the subsequent detection direction. This method allows the equipment to select the detection direction in a targeted manner according to the characteristics of the scene, improving detection efficiency. For example, in a large conference room, the equipment can determine the key areas to be detected, such as around the conference table and near the audio equipment, based on scene recognition.

[0024] More specifically, whether it's user-controlled handheld operation or device-driven autonomous decision-making, the purpose of determining the specified direction is to clarify the range of signal acquisition, avoid blind acquisition, and improve the targeting and efficiency of detection. In different scenarios, the location of hidden cameras may vary. By combining user-controlled handheld operation with device-driven autonomous decision-making, we can better adapt to various complex and ever-changing real-world scenarios.

[0025] More specifically, before acquiring infrared light signals, an initial signal acquisition sensitivity needs to be set for the signal detection device. This sensitivity can be preset according to different application scenarios and detection needs. For example, in relatively open environments with less interference, a lower sensitivity can be set; while in complex environments with many interference sources, a higher sensitivity needs to be set to ensure that weak infrared signals can be detected. The sensitivity is not fixed. During subsequent detection, the sensitivity will be adaptively adjusted based on whether a hidden camera is detected and the intensity of the camera's infrared signal. At the same time, users can also actively adjust the sensitivity through the touch screen integrated into the signal detection device to meet different detection needs.

[0026] More specifically, appropriate signal acquisition sensitivity ensures the detection of the weak infrared signal emitted by the hidden camera while avoiding excessive environmental interference due to overly high sensitivity settings, thus improving detection accuracy. The intensity of infrared signals and background interference vary significantly in different environments. Dynamically adjusting the sensitivity allows the device to maintain good detection performance in various conditions.

[0027] More specifically, the signal detection device uses its built-in infrared sensor to collect infrared light signals in a specified direction according to the set signal acquisition sensitivity. The infrared sensor can sense the infrared radiation in the specified direction and convert it into an electrical signal. After processing, the collected electrical signal is recorded to form the original infrared detection data. This data contains information such as the intensity and frequency of the infrared signal at different positions in the specified direction.

[0028] More specifically, the raw infrared detection data is filtered to remove noise and interference signals, thereby improving the quality and reliability of the data. For example, a low-pass filter is used to remove high-frequency noise, making the signal smoother. The filtered electrical signal is then converted into a digital signal and formatted to obtain infrared detection information that can be used for subsequent analysis. This information is presented in a standardized form, which facilitates the subsequent determination of whether a hidden camera exists.

[0029] More specifically, most hidden cameras emit infrared light when they are working. By collecting infrared light signals in a specified direction, these potential signals can be captured, providing a basis for determining whether a hidden camera exists. Infrared light signal collection is a non-contact detection method that will not cause any damage to the environment or equipment being detected, and has good concealment and practicality.

[0030] Specifically, in the second step of the embodiment provided by the present invention, infrared signal feature templates emitted by common hidden cameras are pre-stored in the device's database. These features include the frequency range, intensity range, and fluctuation mode of the signal. Then, the collected infrared detection information is compared with the templates in the database. If some signal features in the detection information highly match the features in the template, it can be preliminarily determined that there may be a hidden camera in the specified direction. Different types of hidden cameras have certain commonalities in design and working principle, and the infrared signals they emit also have specific characteristics. By establishing feature templates and matching them, signals that may come from hidden cameras can be quickly filtered out.

[0031] More specifically, statistical analysis is performed on the collected infrared detection information to determine the normal background distribution of infrared signals in the area, including the average signal intensity and fluctuation range. When the detected infrared signal deviates significantly from the normal background distribution, exhibiting abnormally high-intensity signals, irregular signal fluctuations, or signals of specific frequencies, it is considered a suspicious signal. Further in-depth analysis of the suspicious signal, combined with other factors (such as signal duration and location), is conducted to determine whether it is a signal emitted by a hidden camera. In a normal environment, the distribution of infrared signals usually follows a certain regularity; the presence of a hidden camera disrupts this regularity, generating abnormal signals. By analyzing these abnormal signals, hidden cameras can be effectively detected.

[0032] More specifically, in addition to infrared sensors, signal detection equipment can also be equipped with other types of sensors, such as optical cameras and electromagnetic sensors. These sensors combine infrared detection information with data collected from other sensors for comprehensive analysis. For example, when infrared detection indicates a suspicious signal, the image captured by the optical camera can be examined to see if a suspected camera object can be found in the corresponding location. Alternatively, electromagnetic sensors can be used to detect abnormal electromagnetic signals, as hidden cameras generate electromagnetic radiation during operation. Based on the data fusion results from multiple sensors, a more accurate judgment can be made, as the detection results of a single sensor may have errors or limitations. Through multi-sensor fusion, information can be obtained from different perspectives, mutually verifying and supplementing each other, thus improving the accuracy of the judgment.

[0033] More specifically, if the result indicates the presence of a hidden camera, the infrared signal strength of the camera will be analyzed, and the signal acquisition sensitivity will be adaptively adjusted based on the strength to more accurately detect the camera's detailed information. If the result indicates the absence of a hidden camera, the probability of the camera being hidden will be analyzed based on the infrared detection information, and the signal acquisition sensitivity will be adjusted based on the analysis results to prepare for subsequent infrared detection.

[0034] Specifically, in the third step of the embodiment provided by the present invention, infrared signal data related to the hidden camera that is determined to exist is extracted from the previously collected infrared detection information. Since infrared signal information from multiple locations may be acquired simultaneously when collecting infrared light signals, it is necessary to accurately identify the part of the signal data corresponding to the hidden camera. A specific algorithm is used to process the extracted infrared signal data and calculate the infrared signal strength of the hidden camera. For example, a value that can accurately reflect the signal strength can be obtained by measuring and calculating parameters such as signal amplitude and power.

[0035] More specifically, when the infrared signal strength of a hidden camera is strong, if the signal acquisition sensitivity is not reduced, the sensor will experience signal saturation, resulting in distorted signals and an inability to accurately obtain detailed information about the signal. By reducing the sensitivity, the sensor can operate within a suitable range, improving detection accuracy. For hidden cameras with weak infrared signal strength, increasing the signal acquisition sensitivity can enhance the sensor's ability to perceive weak signals, allowing signals that were previously ignored to be detected, thus providing a more comprehensive understanding of the hidden camera's situation.

[0036] More specifically, the calculated infrared signal strength is compared with the current signal acquisition sensitivity. If the signal strength is strong, it means the current sensitivity is too high, making it susceptible to interference during subsequent detection and wasting equipment resources. If the signal strength is weak, it means the current sensitivity is too low, hindering accurate detection of the hidden camera. Based on the comparison results, the signal acquisition sensitivity is adjusted accordingly: if the signal strength is strong, the sensitivity is appropriately reduced; if the signal strength is weak, the sensitivity is increased. The adjustment range can be determined according to preset rules, such as setting a certain proportional coefficient to adjust the sensitivity value proportionally based on the difference between the signal strength and the sensitivity.

[0037] More specifically, excessively high signal acquisition sensitivity can cause the device to not only capture signals from hidden cameras but also introduce more environmental interference signals, increasing the difficulty of subsequent data processing and analysis. By adjusting the sensitivity according to the actual signal strength, unnecessary interference signals can be reduced, detection efficiency can be improved, and the device can allocate resources reasonably while meeting detection requirements, avoiding excessive data acquisition, reducing power consumption and processing burden, and extending the service life of the device.

[0038] More specifically, in actual testing, different hidden cameras have different infrared signal strengths, and the signal strength of the same camera will also change under different environmental conditions. By analyzing the signal strength in real time and adjusting the sensitivity, the equipment can better adapt to various complex and changing scenarios, ensuring the accuracy and reliability of the detection.

[0039] Specifically, in the fourth step of the embodiment provided by the present invention, the infrared detection information is analyzed to express the infrared signal status at each specific location in the specified direction in a vectorized form. For example, the infrared signal intensity, frequency and other features at each location are represented by vectors. By combining the vector information of all locations, an infrared detection feature matrix in the specified direction is obtained. This matrix comprehensively reflects the distribution of infrared signals in the specified direction.

[0040] More specifically, an adjacency matrix is ​​constructed based on the infrared detection feature matrix. The adjacency matrix is ​​used to describe the relationship between each element in the matrix. The adjacency matrix is ​​used to analyze the connectivity and centrality of the infrared information at specific locations in a specified direction. Connectivity analysis can determine whether the infrared signals at different locations are related to each other, while centrality analysis can find the key locations of the infrared signals. The connectivity component index and centrality index of each specific location are calculated. These indices can quantitatively reflect the importance and correlation of each location in the entire infrared signal distribution.

[0041] More specifically, based on connectivity and centrality indices, scene patterns are analyzed in a specified direction to determine which scene pattern the infrared signal distribution in that area conforms to, such as open space or complex occlusion. Based on the analysis results, the concealment characteristics of the corresponding scene for the camera's infrared signal are retrieved from the database. Different scenes have different concealment effects on the camera's infrared signal. For example, in scenes with many obstructions, the camera's infrared signal is more easily hidden. Based on the retrieved concealment characteristics, the probability of camera hiding settings is analyzed on the infrared detection feature matrix. Combining the infrared signal characteristics at each position in the matrix with the scene's concealment characteristics, the probability of a hidden camera existing at different positions in a specified direction is calculated, thus obtaining the camera's hiding probability information.

[0042] More specifically, if the analysis results show that there is a high probability of hidden cameras in certain areas, the signal acquisition sensitivity of these areas can be increased. This can enhance the device's ability to capture weak infrared signals in these areas and make it more likely to discover hidden cameras. For areas with a low probability of being hidden, the signal acquisition sensitivity can be appropriately reduced. On the one hand, this can reduce the power consumption and data processing volume of the device, and on the other hand, it can avoid too many interference signals affecting the detection results.

[0043] More specifically, by conducting in-depth analysis of infrared detection information, high-probability areas where hidden cameras may exist can be identified, and the signal acquisition sensitivity in these areas can be improved. This enhances the device's ability to perceive weak infrared signals, increasing the chances of detecting even very weak signals from hidden cameras, thereby improving detection accuracy. Reducing sensitivity in low-probability areas reduces interference from irrelevant infrared signals in the environment, allowing the device to focus more on detecting high-probability areas, further improving detection accuracy. Adjusting the signal acquisition sensitivity based on the concealment probability information allows the device to concentrate more resources on detecting high-probability areas, avoiding wasting too much time and resources in low-probability areas, thus improving detection efficiency. Reducing the sensitivity in low-probability areas reduces the amount of useless data collected, thereby reducing the workload of subsequent data processing and analysis, and accelerating the detection speed.

[0044] More specifically, the concealment methods and infrared signal characteristics of hidden cameras may vary in different scenarios. By analyzing the infrared detection information in real time and adjusting the sensitivity, the device can better adapt to various complex and changing scenarios and improve its ability to detect hidden cameras in different environments.

[0045] Furthermore, the method for adjusting the signal acquisition sensitivity also includes active adjustment. Specifically, the active adjustment method involves interacting with the device via a touchscreen integrated on the signal detection device to generate an adjustment command for the signal acquisition sensitivity, thereby actively adjusting the signal acquisition sensitivity.

[0046] Specifically, after turning on the signal detection device, if the user needs to adjust the signal acquisition sensitivity, they can wake up the interactive interface on the touch screen by touching or clicking. This interface usually displays relevant information about the current signal acquisition sensitivity, such as the specific value of the sensitivity and the current level, so that the user can understand the current working status of the device.

[0047] More specifically, the touchscreen interface will have a dedicated area or button for adjusting the signal acquisition sensitivity. Users can adjust the signal acquisition sensitivity by clicking buttons such as "increase sensitivity" or "decrease sensitivity", or by sliding the slider or entering specific values, according to their own judgment and needs.

[0048] More specifically, after the user completes the command input, the touch screen transmits the command to the control system of the signal detection device. After receiving the command, the control system will parse and verify the command to confirm its validity. If the command is valid, the control system will adjust the relevant parameters inside the signal detection device according to the command content, thereby adjusting the signal acquisition sensitivity. After the adjustment is completed, the device will update the sensitivity information displayed on the touch screen and provide feedback on the adjustment result to the user.

[0049] More specifically, in some special environments, such as places with strong infrared interference sources (such as high-power infrared lighting equipment), the sensitivity of the equipment's automatic adjustment may not meet the detection requirements. In this case, users can actively reduce the sensitivity to reduce the impact of interference signals and improve the accuracy of detection. In some environments with extremely weak infrared signals, users can actively increase the sensitivity to enhance the equipment's ability to capture weak signals and increase the probability of detecting hidden cameras.

[0050] More specifically, different users may have different focuses and accuracy requirements for detection. Some users pay more attention to comprehensive coverage of the detection range and hope to quickly screen within a large area. In this case, the sensitivity can be appropriately reduced to expand the detection range. On the other hand, some users are more concerned with detailed detection of specific areas, so the sensitivity can be increased to perform more detailed detection of that area. When the device's automatic adjustment mechanism makes a misjudgment or is not timely in adjusting, users can actively adjust it to correct or supplement it. For example, if the device judges that no sensitivity adjustment is needed based on the current detection results, but the user suspects the existence of a hidden camera based on their own experience or other clues, they can actively increase the sensitivity to perform a more in-depth detection.

[0051] Furthermore, the touch screen has a screen display function and a screen magnification function;

[0052] The screen display function is used to acquire optical vision data in a specified direction and display the acquired optical vision data in the specified direction. At the same time as displaying the optical vision data, the infrared detection information is also displayed.

[0053] The image magnification function is used to selectively magnify the data displayed on the touch screen and perform scene recognition on the magnified part to generate camera hiding probability information in that scene. Furthermore, based on the camera hiding probability information, the signal acquisition sensitivity is temporarily adjusted to perform infrared detection on the magnified part of the image.

[0054] Specifically, the signal detection device is equipped with an optical camera that can collect optical visual data in a specified direction. This process is similar to taking a picture with an ordinary camera, recording the scene in the specified direction in the form of images or videos. The device transmits the collected optical visual data to the touch screen for display. At the same time, the previously collected infrared detection information is also displayed on the screen. For example, areas with stronger infrared signals will be marked on the optical image with different colored markers.

[0055] More specifically, displaying optical visual data and infrared detection information simultaneously allows users to more intuitively understand the actual situation of the detection area. Users can combine the scene layout in the optical image with the distribution of infrared signals to more accurately determine whether there is a hidden camera. For example, if an ordinary-looking decoration is seen in the optical image, but an abnormal signal is shown in the infrared detection information, then this decoration may be a disguise for a hidden camera. Optical visual data can provide background and reference for infrared detection information, helping users better understand the meaning of infrared signals. For example, if the infrared signal shows an anomaly in a certain area, but a normal infrared light source (such as a heating device) is found in that area in the optical image, the possibility of a hidden camera in that area can be ruled out.

[0056] More specifically, users can selectively zoom in on the displayed data using the touchscreen, similar to zooming in on a smartphone image, through common touch operations like two-finger zoom. The device then performs scene recognition on the zoomed-in portion of the image. Utilizing built-in image recognition algorithms, it analyzes features such as objects and layout within the zoomed-in image to determine the scene type, such as a bedroom or bathroom scene. Based on the scene recognition results, the device retrieves common camera hiding locations and probability information from its database for that scene. For example, in a bedroom scene, cameras are more likely to be hidden in objects like bedside lamps or alarm clocks. The device generates the probability of camera hiding in different locations within that scene based on this information. Based on this generated camera hiding probability information, the device temporarily adjusts the signal acquisition sensitivity: increasing sensitivity in areas with a high hiding probability and decreasing sensitivity in areas with a low hiding probability. Then, it re-performs infrared detection on the zoomed-in portion of the image to more accurately locate hidden cameras.

[0057] More specifically, through the image magnification function, users can focus on the details of the detection area and discover objects or areas that are easily overlooked in the overall image. These details are key clues to hidden cameras. Scene recognition and concealment probability analysis allow the device to adjust the signal acquisition sensitivity according to the characteristics of different scenes, thereby improving the accuracy and efficiency of detection. For example, in a bathroom scene, by increasing the sensitivity of common concealed locations such as mirrors and vents, hidden cameras can be detected more effectively.

[0058] More specifically, these two functions work together to significantly improve the detection capabilities of signal detection equipment. The screen display function provides comprehensive detection information, giving users a macroscopic understanding of the detection area; while the screen magnification function delves into the details, enabling meticulous detection through precise sensitivity adjustments, greatly increasing the probability of discovering hidden cameras.

[0059] Furthermore, the designated direction for collecting infrared light signals is determined not only by the orientation of the handheld control signal detection device held by the user, but also by the autonomous determination of the signal detection device itself. Specifically:

[0060] The orientation of the signal detection device is controlled by a gimbal mounted on the signal detection device, so that the signal detection device is oriented in the correct direction.

[0061] The method further includes, before the gimbal controls the orientation of the signal detection device:

[0062] Scene recognition is performed on optical vision data in a specified direction, and the specified direction is divided into several sub-regions based on the scene recognition results, and corresponding detection weights are configured for each sub-region.

[0063] The detection value of each neighboring orientation in a specified direction is analyzed based on the detection weight of each sub-region, and the subsequent orientation of the signal detection device is determined based on the analysis results.

[0064] Specifically, the signal detection equipment uses its own optical camera to collect optical visual data in a specified direction, acquiring scene images or video information in that direction. The equipment uses advanced image recognition algorithms to analyze and process the collected optical visual data, identifying the type and characteristics of the scene. For example, it can determine whether the scene is a hotel room, a conference room, or an office, as well as the distribution of objects such as furniture, appliances, and decorations in the scene. The possible locations and probabilities of hidden cameras appearing vary in different scenes. Through scene recognition, the equipment can perform subsequent detection work in a more targeted manner.

[0065] More specifically, based on the scene recognition results, the specified direction is divided into several sub-regions with different characteristics and functions. For example, in a hotel room scene, the bed area, bathroom area, TV cabinet area, etc., can be divided into different sub-regions. Each sub-region is assigned a corresponding detection weight. The determination of the detection weight is usually based on the probability of a hidden camera in the scene. For example, the area around the bathroom and bed is a relatively easy place to hide a camera, so these sub-regions can be assigned a higher detection weight. On the other hand, some relatively open areas where it is not easy to hide objects can have a relatively lower detection weight. By dividing the area and configuring the weight, the key detection areas can be highlighted, detection resources can be allocated reasonably, and detection efficiency can be improved.

[0066] More specifically, based on the detection weight of each sub-region, the detection value of each neighboring orientation in a specified direction is analyzed. The detection value mainly considers factors such as the proportion of high-weight sub-regions in neighboring orientations and the importance of the sub-regions. For example, if a neighboring orientation contains multiple high-weight sub-regions, then the detection value of that orientation is relatively high. The signal detection device determines the subsequent orientation based on the results of the detection value analysis. After receiving the instruction from the device, the pan-tilt unit controls the signal detection device to rotate to the corresponding orientation in order to carry out the next round of infrared light signal acquisition. Prioritizing the detection of orientations with high detection value makes it more likely that the device will discover hidden cameras and avoids wasting too much time and resources in low-value areas.

[0067] More specifically, the device autonomously determines the collection direction, covering areas overlooked by users, ensuring comprehensive detection, and reducing the possibility of missing hidden cameras. Through scene recognition, sub-region division, and detection value analysis, the device can selectively choose the detection direction and rationally allocate detection resources, greatly improving detection efficiency and saving detection time. This technology can be flexibly adjusted according to different scene characteristics, is suitable for various complex and changeable environments, and has strong versatility and adaptability.

[0068] Furthermore, it also includes: applying a light signal of a specified wavelength in a specified direction to generate reflection in the scene in the specified direction, and when a camera is present in the scene in the specified direction, the reflection will form a bright spot on the lens arc surface of the camera.

[0069] Specifically, the signal detection equipment is equipped with a device that can emit light signals of a specified wavelength. When conducting detection, the equipment emits light of this specific wavelength in a specified direction. This specified wavelength is carefully selected, usually taking into account the reflection characteristics of the camera lens of this wavelength, to ensure that obvious reflective bright spots can be formed on the curved surface of the lens.

[0070] More specifically, when light shines on a scene in a specified direction, if there is a hidden camera in the scene, the light will be reflected on the curved surface of the camera lens. Due to the special structure and optical properties of the lens, the reflected light will concentrate to form one or more bright spots. These bright spots will be more prominent in the surrounding environment and are easily captured by the human eye or the optical sensors of the device.

[0071] More specifically, operators can observe the optical image displayed on the touchscreen to look for any abnormal bright spots. At the same time, the device can also use image recognition algorithms to analyze the acquired optical images, automatically identify bright spots in the image, and determine whether these bright spots match the characteristics of camera lens reflection. For example, by analyzing the shape, brightness, and position of the bright spots, it can determine whether they are bright spots formed by camera lens reflection.

[0072] More specifically, the bright spots formed by reflections are a very intuitive visual signal. Operators do not need professional technical knowledge; they can make a preliminary judgment on the presence of hidden cameras by observing with the naked eye. Even in some complex environments, the bright spots will stand out relatively clearly, reducing the difficulty of detection. As a supplement to infrared light signal detection, this method can corroborate infrared detection. When infrared detection detects abnormal signals but cannot determine the specific location, the bright spots can help quickly locate the position of the hidden camera. Conversely, when the bright spots are not obvious, infrared detection can further confirm the presence of hidden devices. It is applicable to various scenarios, whether it is an indoor hotel room or office, or an outdoor public place. As long as a light signal can be applied in a specified direction, this method can be used for detection, making it highly versatile and adaptable.

[0073] Furthermore, the step of analyzing the probability of camera concealment based on the infrared detection information includes:

[0074] The infrared detection information is analyzed to extract the infrared signal, and the infrared signal status at each specific location in the specified direction is expressed in vector form to obtain the infrared detection feature matrix in the specified direction.

[0075] An adjacency matrix is ​​constructed based on the infrared detection feature matrix, and the connectivity and centrality of the infrared information at each specific location in a specified direction are analyzed based on the adjacency matrix to obtain the connectivity component index and centrality index at each specific location.

[0076] The scene pattern is analyzed in the specified direction based on the connectivity component index and the centrality index, so as to retrieve the concealment characteristics of the corresponding scene for the camera infrared signal from the database based on the analysis results.

[0077] Based on the concealment characteristics, the infrared detection feature matrix is ​​analyzed to determine the probability of camera concealment, thereby obtaining the camera concealment probability information.

[0078] Specifically, the collected infrared detection information undergoes detailed infrared signal analysis. The infrared signal conditions at each specific location in the specified direction, such as signal strength, frequency, and phase, are expressed in a vectorized form. These vectors are combined in order of position to form an infrared detection feature matrix for the specified direction. The matrix form can systematically organize and present the distribution of infrared signals in the specified direction, facilitating subsequent mathematical analysis and processing. Each vector represents the infrared signal characteristics at a specific location, while the matrix reflects the overall picture of the infrared signals in the entire specified direction.

[0079] More specifically, an adjacency matrix is ​​constructed based on the infrared detection feature matrix. The adjacency matrix mainly describes the degree of correlation between infrared signals at various locations. If the infrared signal characteristics of two locations are similar, their corresponding element values ​​in the adjacency matrix will be larger; otherwise, they will be smaller. By analyzing the adjacency matrix, the connectivity of infrared information at specific locations in a specified direction can be determined. The connectivity index reflects whether the infrared signals at different locations are interconnected and form a whole. For example, if the infrared signals at some locations are interconnected, it means that these locations are affected by the same infrared source, and there may be hidden cameras.

[0080] More specifically, the centrality index is calculated for each specific location. The centrality index measures the importance of a location within the entire infrared signal network. Locations with high centrality are likely to be key nodes in the infrared signal and are more likely to hide cameras. Connectivity and centrality are important concepts in graph theory. By analyzing them, we can reveal the distribution patterns and internal structure of infrared signals, thereby discovering key locations where hidden cameras may exist.

[0081] More specifically, scene patterns are analyzed for a specified direction based on connectivity and centrality indices. Different scene patterns, such as open spaces, enclosed rooms, and environments with obstructions, exhibit different distribution and propagation characteristics of infrared signals. By analyzing these indices, it is possible to determine which scene pattern the current detection area belongs to. Based on the results of the scene pattern analysis, the concealment characteristics of the corresponding scene for the camera's infrared signals are retrieved from the device's database. The database stores the concealment patterns and characteristics of camera infrared signals under various scenes. For example, in scenes with many obstructions, the camera's infrared signals are more easily blocked and dispersed. Different scenes have different concealment effects on camera infrared signals. Understanding the concealment characteristics of a scene allows for a more accurate assessment of the possibility of camera concealment.

[0082] More specifically, based on the retrieved concealment characteristics, the infrared detection feature matrix is ​​analyzed for the probability of hidden cameras. Combining the infrared signal characteristics of each position in the matrix with the concealment characteristics of the scene, the probability of a hidden camera at each position is comprehensively judged, and the hidden camera probability information is finally obtained. This information is presented in the form of numerical values ​​or probability distributions, clearly showing the probability of a hidden camera at different positions in a specified direction. By comprehensively considering the infrared signal characteristics and the concealment characteristics of the scene, the probability of the existence of hidden cameras can be assessed more scientifically and accurately, providing strong support for subsequent detection and decision-making.

[0083] More specifically, by analyzing and comprehensively considering scene factors from multiple dimensions, the probability of hidden cameras can be assessed more accurately, reducing the possibility of false positives and false negatives. It can be flexibly adjusted according to different scene modes and infrared signal characteristics, making it suitable for various complex and ever-changing detection environments. This provides clear guidance for subsequent signal acquisition sensitivity adjustment and further detection work, improving detection efficiency and effectiveness.

[0084] Based on the technical content of the dynamic signal detection method described in the above-disclosed embodiments, the present invention provides a dynamic signal detection system for implementing the dynamic signal detection method described in any one of the first aspects.

[0085] In this embodiment, the specific implementation of each module in the above system embodiment is described in the above method embodiment, and will not be repeated here.

[0086] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0087] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0088] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A dynamic signal detection method, characterized in that, include: Infrared light signals in a specified direction are collected based on a specified signal acquisition sensitivity to obtain infrared detection information; The infrared detection information is used to determine whether a hidden camera exists in a specified direction; if the determination result is that it exists, the infrared signal strength of the camera is analyzed, and the signal acquisition sensitivity is adaptively adjusted according to the infrared signal strength. If the determination result is that the current sensitivity cannot detect the hidden camera, then the probability of the camera being hidden is analyzed based on the infrared detection information, and the signal acquisition sensitivity is adaptively adjusted based on the analysis result for subsequent infrared detection.

2. The dynamic signal detection method as described in claim 1, characterized in that, The method for adjusting the signal acquisition sensitivity also includes active adjustment. Specifically, the active adjustment method involves interacting with the device via a touch screen integrated on the signal detection device to generate an adjustment command for the signal acquisition sensitivity, thereby actively adjusting the signal acquisition sensitivity.

3. The dynamic signal detection method as described in claim 2, characterized in that, The touchscreen has a display function and a magnification function. The display function is used to acquire optical vision data in a specified direction and display the acquired optical vision data in the specified direction, while simultaneously displaying the infrared detection information. The magnification function is used to selectively magnify the data displayed on the touchscreen and perform scene recognition on the magnified portion to generate camera hiding probability information in the recognized scene. Furthermore, based on the camera hiding probability information, the signal acquisition sensitivity is temporarily adjusted to perform infrared detection on the magnified portion of the image.

4. The dynamic signal detection method as described in claim 1, characterized in that, The designated direction for collecting infrared light signals is determined not only by the orientation of the signal detection device held by the user, but also by the autonomous direction of the signal detection device itself. Specifically, this is achieved by controlling the orientation of the signal detection device via a gimbal mounted on it. Before the gimbal controls the orientation, the process includes: performing scene recognition on the optical visual data of the designated direction; dividing the designated direction into several sub-regions based on the scene recognition results; and assigning corresponding detection weights to each sub-region. Based on the detection weights of each sub-region, the detection value of each neighboring orientation of the designated direction is analyzed to determine the subsequent orientation of the signal detection device.

5. The dynamic signal detection method as described in claim 1, characterized in that, Also includes: A light signal of a specified wavelength is applied in a specified direction to generate a reflection in the scene in that direction. When a camera is present in the scene in the specified direction, the reflection will form a bright spot on the curved surface of the lens of the camera.

6. The dynamic signal detection method as described in claim 1, characterized in that, The steps for analyzing the camera hiding probability based on the infrared detection information include: parsing the infrared signals of the infrared detection information and expressing the infrared signal status at specific locations in a specified direction in a vectorized form to obtain an infrared detection feature matrix for the specified direction; constructing an adjacency matrix based on the infrared detection feature matrix, and parsing the connectivity and centrality of the infrared information status at specific locations in the specified direction based on the adjacency matrix to obtain the connectivity component index and centrality index for each specific location; analyzing the scene mode in the specified direction based on the connectivity component index and the centrality index, and retrieving the corresponding scene's concealment characteristics for the camera's infrared signals from the database based on the analysis results; and performing a probability analysis of camera hiding settings on the infrared detection feature matrix based on the concealment characteristics to obtain camera hiding probability information.

7. A dynamic signal detection system, characterized in that, A dynamic signal detection method for implementing any one of claims 1-6.

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