Vehicle monitoring method based on frame difference and deep learning fusion

Abstract

The invention discloses a vehicle monitoring method based on frame difference and deep learning fusion, and relates to the technical field of vehicle monitoring, cameras and environment sensors are deployed in a monitoring area, and videos and multi-source data are acquired by means of vehicle-road cooperation; a self-adaptive frame difference method is used, morphology and optical flow estimation are matched, a threshold value is determined according to the environment, and vehicle features are extracted; constructing a deep convolutional neural network with an attention mechanism, and training a model by using various data in combination with migration and reinforcement learning; fusing the two types of features based on a graph attention network to form high-quality fusion features; a space-time diagram convolutional network is combined with an LSTM to track a vehicle and predict a trajectory, a behavior pattern library is constructed to judge abnormity, and classification analysis is performed in combination with an SVM and a knowledge graph. According to the invention, the frame difference and deep learning are fused, the monitoring accuracy is improved, and the vehicle can be accurately identified and detected; the real-time performance is enhanced, the data is quickly processed, and the environmental influence is reduced; traffic management is assisted, and a safe and efficient traffic environment is created.

Description

Technical Field

[0001] The present invention relates to the field of vehicle monitoring technology, and in particular to a vehicle monitoring method based on the fusion of frame difference and deep learning. Background Art

[0002] With the rapid development of the transportation industry and the increasing number of vehicles, traffic management faces significant challenges. Traditional vehicle monitoring methods, such as manual patrols and fixed-point camera surveillance, suffer from inefficiency, limited coverage, and an inability to obtain comprehensive, real-time vehicle information. In complex traffic scenarios, such as rush hour and inclement weather, these traditional methods struggle to meet the demands for precise vehicle monitoring and management, leading to a range of issues, including traffic congestion and delayed accident handling.

[0003] In recent years, video surveillance technology has been widely used in the field of vehicle monitoring. Video-based vehicle monitoring methods analyze video data captured by cameras to obtain vehicle information. Frame difference analysis is a commonly used technique. It detects vehicle motion by calculating the differences between adjacent video frames. While this method can quickly locate moving vehicles, it is susceptible to environmental factors (such as lighting changes and background interference), resulting in inaccurate detection results and difficulty in obtaining detailed vehicle feature information.

[0004] The rise of deep learning technology has brought new development opportunities to vehicle monitoring. Deep learning models, particularly convolutional neural networks, possess powerful capabilities in image recognition and feature extraction, accurately identifying vehicle type, color, license plate, and other information. However, relying solely on deep learning for vehicle monitoring presents challenges such as high computational resource requirements and poor detection of small and occluded vehicles. Furthermore, training deep learning models requires large amounts of labeled data, resulting in high data collection and annotation costs. Therefore, effectively integrating frame subtraction and deep learning technologies to leverage their respective strengths and improve the accuracy, real-time nature, and robustness of vehicle monitoring has become a pressing issue. Summary of the Invention

[0005] The present invention proposes a vehicle monitoring method that integrates frame difference and deep learning to solve the problems mentioned in the above-mentioned prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a vehicle monitoring method based on the fusion of frame difference and deep learning, comprising:

[0007] Comprehensive data collection: Cameras with shooting and night vision capabilities are deployed throughout the monitoring area, capturing video data at a frame rate of 30-60 frames per second, covering different time periods, weather conditions, and lighting conditions. Environmental sensors are also deployed to collect light, temperature, humidity, wind speed and direction parameters, and vehicle status data from onboard equipment is acquired using vehicle-infrastructure collaboration technology.

[0008] Improved frame difference feature extraction: A frame difference method with an adaptive threshold is used for consecutive video frames. The frame difference threshold is dynamically determined using the formula T = f(S, E), combining historical frame difference statistics with current environmental parameters. Here, S is the historical frame difference data, E is the environmental parameter vector, and f is a function trained based on machine learning.

[0009] Deep neural network construction and training: Build a deep convolutional neural network that combines attention mechanism and feature fusion; collect vehicle image and video data, including different vehicle models, colors, and driving status; use a combination of transfer learning and reinforcement learning for training, and use the focal loss function to optimize the model.

[0010] Furthermore, it also includes:

[0011] Sensor fusion enhancement steps: Fusion of millimeter wave radar, lidar data and video data; using the extended algorithm of Kalman filtering, the prediction equation formula is The Kalman gain calculation equation is: in is the state estimation, F is the state transfer matrix, B is the control matrix, u is the control vector, K is the Kalman gain, P is the covariance matrix, H is the observation matrix, and R is the observation noise covariance matrix;

[0012] Modal feature fusion optimization: The motion features extracted by the frame difference method and the semantic features extracted by the deep learning model are fused through a fusion strategy based on the graph attention network (GAT). GAT is used to calculate the attention weights between features. The formula is: where α ij is the attention weight between nodes i and j, a is the attention vector, W is the weight matrix, h i 、h j is the node feature, is the set of neighbor nodes of node i;

[0013] Target tracking and trajectory prediction: Based on fusion features, the space-time graph convolution network ST-GraphNet is used for tracking; the position, appearance, and time series information of the vehicle in the video frame are constructed into a space-time graph, and the graph convolution operation is used to perform the tracking. Update node features to achieve tracking, where X l is the node feature of the lth layer, is the adjacency matrix, is the degree matrix, W l is the weight matrix, σ is the activation function; combined with the long short-term memory network LSTM to learn the vehicle's historical trajectory, the formula h t =f(W xh x t +W hh h t-1 +b h ) predicts the future trajectory of the vehicle, where h t is the hidden state at time t, x t is the input at time t, W xh 、W hh is the weight matrix, b h is the bias vector, f is the activation function;

[0014] Abnormal behavior detection and classification: Establish a normal vehicle behavior pattern library based on the generative adversarial network (GAN) and clustering algorithm, learn the normal behavior distribution through adversarial training; calculate the Euclidean distance between the current vehicle behavior characteristics and the patterns in the pattern library Exceeding the threshold is considered abnormal, where x i is the current vehicle behavior feature vector, y i The normal behavior pattern feature vector is used to classify abnormal behaviors using support vector machine (SVM) combined with knowledge graph technology, and the type and cause of abnormal behaviors are determined based on traffic rules and vehicle relationship knowledge graphs.

[0015] Real-time feedback and adaptive adjustment steps: Establish a real-time evaluation mechanism for system performance, and evaluate the monitoring effect based on detection accuracy, tracking precision, and response time indicators; when performance degrades, automatically collect new data, use online learning algorithms to fine-tune the deep learning model, and dynamically adjust the parameters of the frame difference method and tracking algorithm according to environmental changes and traffic flow, and adjust the adaptive threshold and tracking window size through formulas to ensure that the system maintains monitoring performance.

[0016] Furthermore, in the all-round data collection step, cameras are deployed in distributed clusters, and edge computing technology is used to perform preliminary screening and preprocessing of data locally; according to the complexity and degree of change of the video content, data transmission priority and bandwidth allocation are dynamically adjusted through formulas to reduce unnecessary data transmission.

[0017] Furthermore, in the multi-sensor fusion enhancement step, deep learning-assisted sensor data calibration technology is introduced; convolutional neural networks are used to extract and analyze features of the raw data of millimeter-wave radar and lidar, and compared with historical calibration data. The sensor parameters are automatically adjusted through optimization algorithms to compensate for sensor errors and drift.

[0018] Furthermore, in the real-time feedback and adaptive adjustment steps, reinforcement learning is used to optimize the adaptive strategy; the system performance indicator is used as the reward function, and different parameter adjustment strategies are used as actions. Through continuous interactive learning with the environment, the system can automatically select the adjustment strategy.

[0019] Furthermore, in the deep neural network construction and training steps, the knowledge distillation technology is used to implement the knowledge transfer of the pre-trained model to the lightweight model; the teacher model learns rich knowledge on a large-scale data set, and the student model is trained by minimizing the output difference with the teacher model. The formula is L kd =αL ce +(1-α)L distill , where L kd is the knowledge distillation loss, L ce is the cross entropy loss, L distill is the distillation loss, and α is the weight coefficient.

[0020] Furthermore, in the multi-target tracking and trajectory prediction steps, the interaction between vehicles is taken into account; a vehicle interaction relationship diagram is constructed, and the graph neural network (GNN) is used to learn the relative position, speed, and intention interaction features between vehicles; in the tracking and prediction process, the vehicle motion model and prediction results are adjusted according to the interaction features to handle following and overtaking behaviors in vehicle-dense scenarios.

[0021] Furthermore, in the abnormal behavior detection and classification steps, time series analysis and causal reasoning are combined; time series modeling is performed on vehicle behavior data, and the autoregressive integrated moving average model (ARIMA) is used to analyze behavior trends and periodicity.

[0022] Furthermore, after the multimodal feature fusion optimization step, a semantic segmentation step is added; the fused features are processed using a semantic segmentation neural network to segment the vehicle from the background, and the various vehicle components are semantically labeled.

[0023] Compared with the existing technology, the beneficial effects of the present invention are:

[0024] In terms of accuracy, the system combines the advantages of frame difference and deep learning. The frame difference method quickly captures vehicle motion information, providing the deep learning model with clues about the vehicle's initial position and motion state, enabling it to more specifically extract vehicle features. The deep learning model, in turn, accurately identifies various detailed features of the vehicle. The combination of these two methods significantly improves vehicle detection and recognition accuracy, enabling precise vehicle monitoring in both normal and complex traffic scenarios.

[0025] In terms of real-time performance, the algorithm and data processing process are optimized to reduce computational complexity and processing time. Initial processing using the frame difference method quickly identifies moving areas, reducing the amount of data required by the deep learning model. Furthermore, the lightweight deep learning model structure and edge computing technologies enable rapid data processing and analysis, enabling real-time vehicle tracking and abnormal behavior detection, enabling timely identification of traffic anomalies.

[0026] In terms of robustness, this method effectively reduces the impact of environmental factors on monitoring effectiveness. The frame difference method, through strategies such as adaptive threshold adjustment, reduces false detections caused by factors such as lighting changes and background interference. The deep learning model, trained on a large amount of diverse data, has strong generalization capabilities and maintains good performance under various weather and lighting conditions. Furthermore, the integration of multi-source data fusion and multiple sensors further enhances the system's monitoring capabilities in harsh environments.

[0027] In terms of expanded functionality, this method not only enables basic vehicle monitoring but also enables in-depth analysis of vehicle behavior patterns and accurate detection of various abnormal behaviors. Furthermore, this method provides rich data support for traffic management, helping to optimize traffic flow, plan transportation facilities, and enhance the intelligence level of traffic management, laying a solid foundation for creating a safer and more efficient transportation environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a schematic block diagram of the vehicle monitoring method integrating frame difference and deep learning proposed in the present invention;

[0029] Figure 2 This is a schematic block diagram of the detection accuracy of the vehicle monitoring system under different weather conditions that integrates frame difference and deep learning proposed in the present invention. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0031] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0032] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below with reference to the accompanying drawings.

[0033] Reference Figure 1-2 :A vehicle monitoring method based on the fusion of frame difference and deep learning, comprising the following steps:

[0034] Comprehensive Data Collection: High-definition cameras with multi-angle shooting and night vision capabilities are strategically placed throughout the surveillance area, capturing high-resolution video data at a frame rate of 30-60 frames per second, covering various time periods, weather conditions, and lighting conditions. Environmental sensors are also deployed to collect parameters such as light, temperature, humidity, and wind speed and direction. Vehicle-infrastructure collaboration technology is also used to capture vehicle status data from onboard equipment, providing comprehensive data support for subsequent analysis.

[0035] Improved frame difference feature extraction: A frame difference method with an adaptive threshold is used for consecutive video frames. This method combines historical frame difference statistics with current environmental parameters to dynamically determine the frame difference threshold using the formula T = f(S, E), where S represents historical frame difference data, E represents the environmental parameter vector, and f represents a function trained based on machine learning. After calculating the pixel differences between adjacent frames, morphological processing and optical flow estimation are used to assist in analysis, extracting preliminary features such as the vehicle's direction, speed, and outline.

[0036] Deep Neural Network Construction and Training: We built a deep convolutional neural network that combines an attention mechanism with multi-scale feature fusion. We collected a massive and diverse amount of vehicle image and video data, covering different vehicle models, colors, driving conditions, and complex backgrounds. We used a combination of transfer learning and reinforcement learning for training, optimizing the model with a focal loss function to accurately extract semantic features such as a vehicle's appearance, structure, and logo, and to learn vehicle behavior patterns.

[0037] Multimodal feature fusion optimization: The motion features extracted by the frame difference method and the semantic features extracted by the deep learning model are fused through a fusion strategy based on the graph attention network (GAT). GAT is used to calculate the attention weights between features, and the formula is: where α ij is the attention weight between nodes i and j, a is the attention vector, W is the weight matrix, h i 、h j is the node feature, is the set of neighboring nodes of node i), highlighting key features, suppressing redundant information, and reducing the dimension through principal component analysis (PCA) to form a more discriminative and robust fusion feature. Multi-target tracking and trajectory prediction: Based on the fusion feature, the spatiotemporal graph convolutional network (ST-GraphNet) is used for multi-target tracking. The position, appearance, and time series information of the vehicle in the video frame are constructed into a spatiotemporal graph, and the graph convolution operation is used to perform the multi-target tracking. where X l is the node feature of the lth layer, is the adjacency matrix, is the degree matrix, W l is the weight matrix, σ is the activation function) to update the node features to achieve tracking. Combined with the long short-term memory network (LSTM) to learn the historical trajectory of the vehicle, the formula h is used t =f(W xh x t +W hh h t-1 +b h ), where h t is the hidden state at time t, x t is the input at time t, W xh 、W hh is the weight matrix, b h is the bias vector, and f is the activation function) to predict the future trajectory of the vehicle and handle tracking problems under occlusion and complex traffic scenes.

[0038] Abnormal behavior detection and classification: Establish a normal vehicle behavior pattern library based on generative adversarial networks (GANs) and clustering algorithms, and learn the normal behavior distribution through adversarial training. Calculate the Euclidean distance between the current vehicle behavior characteristics and the patterns in the pattern library. where xi is the current vehicle behavior feature vector, y i The feature vector of the normal behavior pattern is considered abnormal if it exceeds the threshold. The abnormal behavior is classified using support vector machine (SVM) combined with knowledge graph technology, and the type and cause of abnormal behavior are determined based on traffic rules and vehicle relationship knowledge graph.

[0039] The present invention also includes:

[0040] Multi-sensor fusion enhancement step: Fusion of millimeter wave radar, lidar data and video data. Using the extended algorithm of Kalman filter, the formula is (prediction equation), (Kalman gain calculation equation) etc. is state estimation, F is the state transfer matrix, B is the control matrix, u is the control vector, K is the Kalman gain, P is the covariance matrix, H is the observation matrix, and R is the observation noise covariance matrix). This allows for alignment and fusion of different sensor data in time and space, enhancing the accuracy of information such as vehicle distance, speed, and three-dimensional structure, and improving monitoring capabilities in low light and adverse weather conditions.

[0041] Real-time feedback and adaptive adjustment steps: Establish a real-time system performance evaluation mechanism to assess monitoring effectiveness based on metrics such as detection accuracy, tracking precision, and response time. When performance degrades, new data is automatically collected and the deep learning model is fine-tuned using online learning algorithms. Parameters of the frame difference method and tracking algorithm are dynamically adjusted based on environmental changes and traffic flow. For example, adaptive thresholds and tracking window sizes are adjusted using formulas to maintain optimal monitoring performance.

[0042] In this invention, cameras are deployed in a distributed cluster during the omnidirectional data collection process, and edge computing technology is used to perform preliminary local data screening and preprocessing. Based on the complexity and variability of the video content, a formula dynamically adjusts data transmission priority and bandwidth allocation, reducing unnecessary data transmission, improving data processing efficiency and transmission stability, and reducing network load.

[0043] This invention introduces deep learning-assisted sensor data calibration technology into the multi-sensor fusion enhancement step. It uses a convolutional neural network to extract and analyze features from raw data from millimeter-wave radar and lidar, compares it with historical calibration data, and automatically adjusts sensor parameters through an optimization algorithm to compensate for sensor errors and drift, further improving the accuracy and reliability of the fused data.

[0044] In this invention, reinforcement learning is used to optimize the adaptive strategy during the real-time feedback and adaptive adjustment steps. System performance indicators are used as reward functions, and different parameter adjustment strategies are used as actions. Through continuous interactive learning with the environment, the system can automatically select the optimal adjustment strategy, quickly adapt to complex and changing traffic scenarios and environmental conditions, and achieve intelligent adaptive optimization of the monitoring system.

[0045] In this invention, in the deep neural network construction and training steps, the knowledge distillation technology is used to transfer the knowledge of the large pre-trained model to the lightweight model. The teacher model learns rich knowledge on a large-scale data set, and the student model is trained by minimizing the difference between the output and the teacher model. The formula is L kd =αL ce +(1-α)L distill , where L kd is the knowledge distillation loss, L ce is the cross entropy loss, L distill is the distillation loss, α is the weight coefficient), while ensuring the performance of the model, it reduces the demand for computing resources and facilitates real-time deployment and operation on edge devices.

[0046] In this invention, the interaction between vehicles is considered during the multi-target tracking and trajectory prediction steps. A vehicle interaction graph is constructed, and a graph neural network (GNN) is used to learn interaction features such as relative position, speed, and intent between vehicles. During the tracking and prediction process, the vehicle motion model and prediction results are adjusted based on these interaction features. This allows for more accurate handling of behaviors such as following and overtaking in densely populated vehicle scenarios, improving the accuracy and stability of multi-target tracking.

[0047] In this invention, time series analysis and causal reasoning are combined in the abnormal behavior detection and classification steps. Time series modeling is performed on vehicle behavior data, and the Autoregressive Integrated Moving Average (ARIMA) model is used to analyze behavioral trends and periodicity. Causal reasoning algorithms (such as Granger causality tests) are used to explore the causal relationships between vehicle behaviors and determine the causal chain of abnormal behavior, such as the correlation between traffic congestion and illegal lane changes, providing more in-depth decision-making basis for traffic management.

[0048] In this paper, a semantic segmentation step is added after the multimodal feature fusion optimization step. A semantic segmentation neural network is used to process the fused features, accurately segmenting the vehicle from the complex background. Simultaneously, vehicle components (such as wheels, body, and windows) are semantically annotated. The segmentation and annotation results are used for further vehicle feature analysis, anomaly detection, and behavior understanding, improving the level of refined monitoring.

[0049] The above are only preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A vehicle monitoring method based on the fusion of frame difference and deep learning, characterized in that: include: Comprehensive data collection: Cameras with shooting and night vision capabilities are deployed throughout the monitoring area, capturing video data at a frame rate of 30-60 frames per second, covering different time periods, weather conditions, and lighting conditions. Environmental sensors are also deployed to collect light, temperature, humidity, wind speed and direction parameters, and vehicle status data from onboard equipment is acquired using vehicle-infrastructure collaboration technology. Improved frame difference feature extraction: A frame difference method with an adaptive threshold is used for consecutive video frames. The frame difference threshold is dynamically determined using the formula T = f(S, E), combining historical frame difference statistics with current environmental parameters. Here, S is the historical frame difference data, E is the environmental parameter vector, and f is a function trained based on machine learning. Deep neural network construction and training: Constructing a deep convolutional neural network that combines attention mechanism and feature fusion; Collect vehicle images and video data, including vehicle model, color, and driving status; use a combination of transfer learning and reinforcement learning for training, and use the focal loss function to optimize the model.

2. The vehicle monitoring method based on the fusion of frame difference and deep learning according to claim 1 is characterized in that: Also includes: Multi-sensor fusion enhancement step: Fusion of millimeter-wave radar, lidar data and video data; Using the extended algorithm of Kalman filtering, the prediction equation is: The Kalman gain calculation equation is: in is the state estimation, F is the state transfer matrix, B is the control matrix, u is the control vector, K is the Kalman gain, P is the covariance matrix, H is the observation matrix, and R is the observation noise covariance matrix; Multimodal feature fusion optimization: The motion features extracted by the frame difference method and the semantic features extracted by the deep learning model are fused through a fusion strategy based on the graph attention network (GAT). GAT is used to calculate the attention weights between features, and the formula is: where α ij is the attention weight between nodes i and j, a is the attention vector, W is the weight matrix, h i 、h j is the node feature, is the set of neighbor nodes of node i; Multi-target tracking and trajectory prediction: Based on fusion features, tracking is performed using the spatiotemporal graph convolutional network ST-GraphNet; the position, appearance, and time series information of the vehicle in the video frame are constructed into a spatiotemporal graph, and the graph convolution operation is used to perform multi-target tracking. Update node features to achieve tracking, where X l is the node feature of the lth layer, is the adjacency matrix, is the degree matrix, W l is the weight matrix, σ is the activation function; combined with the long short-term memory network LSTM to learn the vehicle's historical trajectory, the formula h t =f(W xh x t +W hh h t-1 +b h ) predicts the future trajectory of the vehicle, where h t is the hidden state at time t, x t is the input at time t, W xh 、W hh is the weight matrix, b h is the bias vector, f is the activation function; Abnormal behavior detection and classification: Build a library of normal vehicle behavior patterns based on generative adversarial networks (GANs) and clustering algorithms, and learn the distribution of normal behaviors through adversarial training. Calculate the Euclidean distance between the current vehicle behavior characteristics and the patterns in the pattern library Exceeding the threshold is considered abnormal, where x i is the current vehicle behavior feature vector, y i The normal behavior pattern feature vector is used to classify abnormal behaviors using support vector machine (SVM) combined with knowledge graph technology, and the type and cause of abnormal behaviors are determined based on traffic rules and vehicle relationship knowledge graphs.

3. The vehicle monitoring method based on the fusion of frame difference and deep learning according to claim 1 is characterized in that: Also includes: Real-time feedback and adaptive adjustment steps: Establish a real-time system performance evaluation mechanism to evaluate monitoring results based on detection accuracy, tracking precision, and response time indicators; When performance degrades, new data is automatically collected and the deep learning model is fine-tuned using online learning algorithms. At the same time, the parameters of the frame difference method and tracking algorithm are dynamically adjusted according to environmental changes and traffic flow. The adaptive threshold and tracking window size are adjusted through formulas to ensure that the system maintains monitoring performance.

4. The vehicle monitoring method based on the fusion of frame difference and deep learning according to claim 1 is characterized in that: In the all-round data collection step, cameras are deployed in distributed clusters, and edge computing technology is used to perform preliminary screening and pre-processing of data locally; according to the complexity and degree of change of the video content, data transmission priority and bandwidth allocation are dynamically adjusted through formulas.

5. The vehicle monitoring method based on the fusion of frame difference and deep learning according to claim 2 is characterized in that: In the sensor fusion enhancement step, deep learning-assisted sensor data calibration technology is introduced; convolutional neural networks are used to extract and analyze features from the raw data of millimeter-wave radar and lidar, and compared with historical calibration data. Sensor parameters are automatically adjusted through optimization algorithms to compensate for sensor errors and drift.

6. The vehicle monitoring method based on the fusion of frame difference and deep learning according to claim 3 is characterized in that: In the real-time feedback and adaptive adjustment steps, reinforcement learning is used to optimize the adaptive strategy; the system performance indicator is used as the reward function, and different parameter adjustment strategies are used as actions. Through continuous interactive learning with the environment, the system automatically selects the adjustment strategy.

7. The vehicle monitoring method based on the fusion of frame difference and deep learning according to claim 1 is characterized in that: In the deep neural network construction and training steps, knowledge distillation technology is used to implement the knowledge transfer of the pre-trained model to the lightweight model; the teacher model learns rich knowledge on a large-scale data set, and the student model is trained by minimizing the output difference with the teacher model. The formula is L kd =αL ce +(1-α)L distill , where L kd is the knowledge distillation loss, L ce is the cross entropy loss, L distill is the distillation loss, and α is the weight coefficient.

8. The vehicle monitoring method based on the fusion of frame difference and deep learning according to claim 2 is characterized in that: In the target tracking and trajectory prediction steps, the interaction between vehicles is taken into account; a vehicle interaction relationship diagram is constructed, and the graph neural network (GNN) is used to learn the relative position, speed, and intention interaction features between vehicles; during the tracking and prediction process, the vehicle's motion model and prediction results are adjusted according to the interaction features to handle following and overtaking behaviors in vehicle-intensive scenarios.

9. The vehicle monitoring method based on the fusion of frame difference and deep learning according to claim 2 is characterized in that: In the abnormal behavior detection and classification steps, time series analysis and causal reasoning are combined; time series modeling is performed on vehicle behavior data, and the autoregressive integrated moving average model (ARIMA) is used to analyze behavior trends and periodicity.

10. The vehicle monitoring method based on the fusion of frame difference and deep learning according to claim 2, characterized in that: After the multimodal feature fusion optimization step, a semantic segmentation step is added; the fused features are processed using a semantic segmentation neural network to segment the vehicle from the background, and the various vehicle components are semantically labeled.

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