184 results about "Pedestrian recognition" patented technology

Disclosed in the present invention is a local feature optimization-based visible light and infrared pedestrian re-identification method. The method comprises: collecting a target area image, the target area image comprising a visible light-mode image and an infrared-mode image; and inputting the target area image into a pedestrian identification model to obtain a pedestrian identification result, wherein the pedestrian identification model comprises a local feature extraction module, a local feature alignment module and a feature collaborative learning module, the local feature extraction module extracts local features of the visible light mode and infrared mode, the local feature alignment module groups and aligns cross-modal local features, so as to establish cross-modal connections, and the feature collaborative learning module enhances each local feature and performs collaborative optimization on the global features and the local features. The present invention can adaptively mine cross-modal local features, directly establish rich alignment relationships for the cross-modal local features, and embed context relationships of global features into the local features, thus remarkably improving the accuracy of pedestrian identification.

The invention belongs to the field of cross-modal pedestrian re-identification, and discloses a cross-modal pedestrian re-identification method based on a coal mine scene, and the method comprises the steps: 1, obtaining an identity representing a pedestrian image and an identity tag corresponding to the pedestrian image, and carrying out the preprocessing; step 2, constructing a cross-modal pedestrian re-identification backbone network; step 3, constructing a multi-scale feature enhancement module and adding the multi-scale feature enhancement module into the cross-modal pedestrian re-identification backbone network; 4, determining a loss function, and completing the final training of the cross-modal pedestrian re-identification backbone network; and 5, taking one modal pedestrian image as a query set, taking the other modal pedestrian image as a to-be-retrieved set, and matching the pedestrian image in the query set with the pedestrian image in the to-be-retrieved set to obtain a cross-modal pedestrian re-identification result. According to the method, cross-modal image features can be effectively fused, feature information of pedestrian images can be effectively mined, and the prediction ability and generalization ability of the model in a cross-modal pedestrian image retrieval scene are effectively improved.

The invention relates to the technical field of computer vision and cross-visual-angle pedestrian recognition, in particular to a cross-visual-angle pedestrian re-recognition method based on visual language prompt learning, and the method comprises the steps: obtaining a target image; the target image is input into a preset pedestrian recognition model, a pedestrian re-recognition result is output, the pedestrian recognition model is obtained by training a visual language pre-training model CLIP through a prompt learning mechanism and a two-stage training strategy, the prompt learning mechanism is used for modeling visual angle deviation, and the two-stage training strategy is used for training visual angle deviation. The double-stage training strategy is used for realizing cross-modal semantic alignment. According to the invention, the accuracy and robustness of cross-view identification can be significantly improved.

The invention discloses a pedestrian recognition method based on video data, and belongs to the field of data processing, and the method comprises the steps: a cloud database sets a plurality of pedestrian IDs and data representations corresponding to the pedestrian IDs; obtaining video data, electing a key frame, and identifying whether the contour feature of the key frame is missing or not; if the key frame is not missing, obtaining a first representation and directly matching the data representation, if the key frame is missing, marking the key frame as a sensitive image, taking N coherent frames associated with the sequence information of the sensitive image to obtain N extended images, performing grid division on the extended images into a plurality of local image blocks to obtain a first image set and a second image set, integrating the feature vectors of the sensitive images into a plurality of vector sets, fusing the first image set and the second image set with the plurality of vector sets according to fusion attributes to obtain a second representation, and matching through the second representation; a video data processing mode is expected to be optimized on the basis of existing pedestrian re-identification, and the problem of feature fusion deviation of the video data in pedestrian re-identification is solved.

The invention discloses a railway pedestrian warning system and method based on an improved YOLOv7 algorithm, and the method comprises the steps: dynamically dividing a warning region according to the GPS positioning of a train and a train timetable, and obtaining a plurality of regions; performing pedestrian recognition on the monitoring area by using a high-resolution camera deployed with a pedestrian target detection model to obtain pedestrian coordinates; wherein the pedestrian target detection model is constructed based on an improved YOLOv7 algorithm; calculating pedestrian dynamic potential energy fields and potential energy gradients in real time according to the pedestrian coordinates and the plurality of areas, and obtaining risk scores according to the potential energy gradients and pedestrian motion vectors; and performing graded early warning and voice warning on the pedestrians according to the risk scores. The invention relates to the technical field of railway security and protection, and solves the technical problems of insufficient detection precision, high false alarm rate and insufficient dynamic risk quantification capability of an existing security and protection system in a complex inter-city railway scene.

The invention discloses a pedestrian re-identification method combined with text guidance, which comprises the following steps: collecting diversified pedestrian images, extracting image features of the images by using a deep convolutional neural network, directly generating text description from an original image through an image description model, extracting text features, and optimizing the comparison loss of the images and the text to obtain the pedestrian re-identification result. Aligning the visual features and the text features; re-recognition classification loss is optimized, it is ensured that the model can effectively distinguish identities of different pedestrians, the performance of the model is evaluated on a verification set, fine adjustment is conducted according to feedback, and the trained model is used for pedestrian recognition. According to the method, the training of the model is supervised by introducing text description, so that the limitation of a traditional pedestrian recognition technology on capturing detail features is solved. According to the method, text description (such as garment styles, colors, textures and the like) is used for guiding and optimizing the training process of a deep learning model, and it is ensured that the model can more accurately learn and recognize the detail features.

The invention belongs to the technical field of computer vision and artificial intelligence, and provides a pedestrian re-identification method for optimizing fine-grained feature fusion, which comprises the following steps: converting an image to be detected into a flattening sequence, and inputting the flattening sequence to a pedestrian re-identification model to obtain pedestrian part features; performing re-identification processing on the pedestrian part features to obtain a pedestrian re-identification result; wherein the trained pedestrian re-identification model comprises a self-adaptive multi-granularity feature fusion encoder which comprises a plurality of layers of Transform encoding modules which are arranged in a stacked manner and more than one multi-granularity feature fusion module; the decoder is used for decoding the pedestrian features to obtain pedestrian part features, the pedestrian part features comprise features of p parts of the pedestrian, fusion of features of different granularities is optimized, and the model performance is improved. The invention also provides a pedestrian re-identification model training method, which promotes the student decoder to absorb the knowledge of the teacher decoder.

The invention relates to the technical field of automatic driving, in particular to an automatic driving system based on pedestrian motivation and a control method, and the system comprises a data acquisition module, a pedestrian detection module, a pedestrian intention reasoning module and a vehicle control module. The data acquisition module is used for acquiring a video sequence and environment information of a traffic intersection when the intelligent driving vehicle drives to the intersection without the signal lamp. The pedestrian detection module detects pedestrians through video processing and a pedestrian recognition strategy and outputs pedestrian inter-frame images. The pedestrian intention reasoning module classifies pedestrian crossing motivations based on the trajectory and attitude information of pedestrians. The vehicle control module dynamically adjusts the vehicle speed according to the pedestrian motivation type and automatically brakes when necessary so as to ensure that pedestrians safely pass through the intersection. According to the system, self-adaptive comity of the intelligent driving vehicle to pedestrians at the intersection without signal lamps is realized, and the traffic safety and the adaptive capacity of intelligent driving are effectively improved.

The present invention discloses a pedestrian recognition method and system based on image and motion behavior prediction, and relates to the field of computer vision technology. The method includes: collecting and analyzing environmental and pedestrian information to obtain target environmental and pedestrian information; predicting the motion behavior trajectory of pedestrians based on the GCN network and DeepSort algorithm in combination with the target environmental and pedestrian information, and generating pedestrian trajectory prediction results; according to the pedestrian trajectory prediction results, the user's motion trajectory is formulated based on the RRT algorithm and the PRM algorithm; according to the pedestrian trajectory prediction results and the user's motion trajectory, the motion trajectory is simulated based on the multi-layer LSTM model and the Seq2Seq model to obtain simulation results; and safety warning information is generated and sent according to the simulation results. The present invention can simulate the scenarios that occur after the pedestrian motion prediction, improve the reliability of the motion route; can analyze and capture information in complex scenarios; and can also warn pedestrians and users during the motion process.

The invention provides an aligned pedestrian re-identification system and method based on boundary progressive sample optimization and a DDT model, and the system comprises an image preprocessing module which is used for carrying out the feature extraction of an existing pedestrian data set based on the DDT model, and obtaining a feature map; the target feature measurement model training module is used for calculating the feature distance of the feature map, performing boundary progressive sample optimization on the existing pedestrian data set based on the feature distance, and training a target feature measurement model; and the cross-camera retrieval module is used for performing target pedestrian recognition and positioning on pedestrian images collected by the monitoring camera based on the trained target feature measurement model in combination with a multi-scale feature alignment technology, obtaining a recognition and positioning result and completing aligned pedestrian re-recognition based on boundary progressive sample optimization and a DDT model. According to the technical scheme of the invention, pedestrians with different backgrounds, different angles and different postures shot by a camera are accurately classified and positioned, and a basis is provided for personnel monitoring and target tracking tasks.

The invention discloses a pedestrian identification method and system based on image and motion behavior prediction, and relates to the technical field of computer vision. The method comprises the following steps: collecting and analyzing environment and pedestrian information to obtain target environment and pedestrian information; based on a GCN network and a DeepSort algorithm, in combination with the target environment and the pedestrian information, predicting the motion behavior track of the pedestrian, and generating a pedestrian track prediction result; according to a pedestrian trajectory prediction result, a motion trajectory of a user is prepared based on an RRT algorithm and a PRM algorithm; according to the pedestrian trajectory prediction result and the user motion trajectory, motion trajectory simulation is carried out based on a multilayer LSTM model and a Seq2Seq model, and a simulation result is obtained; and generating and sending safety warning information according to a simulation result. According to the invention, scenes occurring after pedestrian motion prediction can be simulated, and the reliability of a motion route is improved; information can be analyzed and captured in a complex scene; and pedestrians and users can be warned in the movement process.

The invention discloses a cross-modal pedestrian re-identification and training method, and solves the problems of semantic mismatch, insufficient optimization of difficult cases and unbalanced view angle distribution in visible light-infrared pedestrian re-identification (VI-ReID). The method comprises the following steps: ensuring multi-view data equalization by a view equalization sampler (VB); the basic feature extraction network generates a high-dimensional feature map; a loop semantic aggregation module (RSA) is combined with a bidirectional long-short term memory network (BiLSTM) to optimize local and global features; and constructing a modal alignment loss function to enhance the cross-modal feature consistency. Furthermore, the difficult case recognition capability is improved through a confidence-based difficult case retraining strategy (CHSR). Compared with the traditional method, the method provided by the invention has the advantages that the Rank-1 accuracy and the average precision (mAP) are remarkably improved. The method is suitable for a multi-mode pedestrian re-identification scene, and has important application value in the fields of intelligent monitoring and public safety.

The invention provides a pedestrian re-identification method based on cross-time-domain data, and relates to the technical field of pedestrian identification, and the method comprises the following steps: constructing a monitoring scene; segmenting a monitoring area and a monitoring scene; intercepting a current monitoring video frame to obtain a monitoring image; intercepting an image in the monitoring scene to obtain a background image; a background area in the monitoring image is cut out, and a to-be-processed image is obtained; segmenting the to-be-processed image to obtain an edge image and a center image; capturing a human body target from the edge image to obtain a human body image; giving a unique identity code to the human body target; recognizing the human body image, and marking a recognition result, a correlation mark and an identity code; capturing facial features from the central image to obtain a facial image; acquiring a mark of the human body target; if not, giving a unique identity code to the human body target, recognizing the face image, and obtaining and marking a recognition result; and obtaining, identifying the facial image according to the mark, and obtaining and updating an identification result. And the omission ratio of pedestrians is reduced.

The invention discloses an unsupervised text-to-image pedestrian re-identification method and system. The method comprises the following steps: constructing an image-text matching relationship; clustering operation is carried out on the image features, and false identity tags are distributed to the pedestrian images and the corresponding text descriptions; constructing an image memory dictionary; obtaining a similarity set according to the image memory dictionary; removing pedestrian images with abnormal similarity and corresponding text descriptions according to the similarity set; respectively acquiring image features and text features of pedestrian images and text descriptions, and constructing a multi-stage ternary joint learning process; and obtaining text features to be re-recognized, and performing pedestrian image matching to obtain a pedestrian recognition result. According to the method, the intra-class compactness and inter-class separability of the samples are enhanced, and the retrieval precision and efficiency of the unsupervised text-to-image pedestrian re-recognition method are greatly improved.

The invention belongs to the technical field of traffic safety pre-warning, and provides a pedestrian zebra crossing pre-warning system device which comprises a mounting stand column, a monitoring assembly and a pre-warning assembly, a connecting base is arranged at the top of the front side of the mounting stand column, the monitoring assembly is arranged on the connecting base, and the monitoring assembly comprises an AI camera, a laser radar and a far infrared sensor. The early warning assembly comprises an electronic warning board, a laser transmitter and a laser spotlight; by comprehensively using various sensors such as the AI camera, the infrared sensor and the laser radar, pedestrians can be monitored and recognized from different angles and different modes, the accuracy of pedestrian recognition is improved, and the method is particularly suitable for pedestrian detection under complex weather and illumination conditions and has a wide application prospect. Through the synergistic effect of the laser spotlight, the laser curtain wall, the electronic warning board and the warning sound, early warning is performed on drivers and pedestrians from multiple aspects of visual sense and auditory sense, the early warning effect is enhanced, and the safety of the pedestrians crossing the road is improved.

The invention discloses a video reloading pedestrian re-identification method for constructing a robust auxiliary mode, and aims to improve the identification precision of pedestrians after reloading in a monitoring video, and the method comprises the steps: selecting a video reloading data set to construct a training set; a contour attitude fusion auxiliary enhancement model is constructed, and features irrelevant to clothes are extracted through preprocessing, coding and pedestrian clothes irrelevant feature mining stages; training the model to obtain a predicted ID probability vector; inputting the test video sequence into the model for pedestrian re-identification; according to the method, more robust pedestrian identification is realized by constructing an auxiliary mode for resisting clothing information interference and clothing change influence.

The embodiment of the invention relates to the technical field of data processing, in particular to a pedestrian re-identification method, device and equipment and a storage medium, and aims to improve the performance of a pedestrian re-identification model. The method comprises the following steps: selecting pedestrian images of various categories to obtain a first image combination; pedestrian identity features of the pedestrian images in the first image set are extracted, and an identity feature relation graph is constructed; according to the relation graph, forming a second image set by the pedestrian images of each category and the corresponding images of adjacent categories; pedestrian change features of the pedestrian images in the second image set are extracted; forming a third image set by each sample image and the pedestrian images of different pedestrian categories; fusing the pedestrian identity features of the sample image with the pedestrian change features of the other images to obtain generation features; combining the generated features with the original features to obtain a corresponding training set; using the training set to train a pedestrian re-identification model; and obtaining a pedestrian recognition result through the pedestrian re-recognition model.

The invention discloses a pedestrian tumble detection method based on NanoDet-Plus and fine tuning CLIP. The method comprises the following steps: S1, collecting data to make a data set; s2, a NanoDet-Plus target detection model is trained, and a trained algorithm model weight file is obtained; s3, performing pedestrian recognition on the image by using a target detection model NanoDet-Plus, and independently storing the recognized pedestrian image as an image file; s4, training the CLIP model and the improved Resnet50 model; s5, performing preliminary classification prediction on the pedestrian attitude image by using a CLIP model, performing further classification prediction by using an improved Resnet50 model, and connecting two classification prediction results so as to obtain a pedestrian attitude prediction result; and judging the current state of the pedestrian according to a posture prediction result, and taking feedback measures. And the recognition accuracy of the pedestrian falling event in the complex scene is improved.

The invention provides a pedestrian re-identification method based on three-dimensional reconstruction and image generation, and belongs to the technical field of machine vision. According to the invention, pedestrian images in an original pedestrian image set are utilized to generate a corresponding three-dimensional human body model; selecting a specific angle of the three-dimensional human body model to generate a two-dimensional pedestrian rendering graph; generating a virtual pedestrian image in combination with the two-dimensional pedestrian rendering graph and the original pedestrian image; adding the virtual pedestrian image into the original pedestrian image set to construct an enhanced image set; training a pedestrian re-identification model based on the enhanced image set; and inputting a to-be-detected pedestrian image to the trained pedestrian re-identification model to obtain a pedestrian identification result. According to the invention, the original pedestrian image set is enriched by generating images of different angles of portraits in the original pedestrian image set, and the problem of scarcity of pedestrian re-identification data is solved; and the model is trained and updated on the enhanced data set in combination with classification loss and triple loss, so that the model can correctly learn the generated image information, and the pedestrian re-identification precision is improved.

The present invention provides a multi-model pedestrian recognition method for highway videos based on convolutional neural networks, including obtaining a video stream through a surveillance camera and intercepting continuous frame images; performing multi-environment compensation preprocessing on the images to generate a standardized feature map group; inputting the feature map group into three parallel convolutional neural network models to extract spatial, motion and context feature matrices respectively; fusing the feature matrices through a dynamic feature fusion module to obtain a comprehensive feature tensor; adaptively weighting the comprehensive feature tensor based on the real-time environmental parameters output by the environmental perception module to generate an environmental compensation feature tensor; performing multi-level analysis on the feature tensor using a cascade classifier to output an initial recognition result; and finally performing spatiotemporal consistency verification on the recognition results of continuous frames through a time series verification module to generate a final recognition result. The present invention can improve the accuracy and robustness of highway video pedestrian recognition in complex environments and improve system reliability.

The utility model discloses a coal mine roadheader personnel approach alarm power-off device which comprises an inverted T-shaped supporting block and a rectangular-ambulatory-plane limiting frame fixed to the top of the T-shaped supporting block, a pedestrian recognition device is detachably installed in the rectangular-ambulatory-plane limiting frame, and a U-shaped frame capable of horizontally moving is arranged above the rectangular-ambulatory-plane limiting frame. A supporting plate is fixedly connected into the U-shaped frame, a high-pressure air pump is fixedly installed on the supporting plate, the output end of the high-pressure air pump is fixedly connected with an air conveying hose, and a main spraying pipe is rotationally arranged between the inner walls of the U-shaped frame through a rotating shaft. According to the device, the high-pressure air pump is started, high-pressure air is conveyed into the main spraying pipe through the air conveying hose and finally sprayed out through the multiple nozzles, the high-pressure air is sprayed to the surface of a lens of the pedestrian recognition device, dust accumulated on the surface is removed, and the second motor is started to drive the main spraying pipe and the nozzles to rotate; therefore, the lens of the pedestrian recognition device is fully cleaned, and accurate recognition of the lens is ensured.

The present invention relates to a training method, device, equipment and medium for a pedestrian recognition model, belonging to the field of artificial intelligence technology, and the method includes: constructing a semantic feature encoder integrated with an attention mechanism; extracting semantic features of a sample data set through the semantic feature encoder; parsing the sample data set to obtain body shape features; obtaining KL divergence loss and feature difference loss based on the semantic features and the body shape features; performing weighted calculation based on at least KL divergence loss and feature difference loss to obtain model loss parameters; training a pedestrian recognition model to be trained according to the model loss parameters until the training converges to obtain a trained pedestrian recognition model. The scheme of the present invention extracts the semantic features of the original pedestrian more fully by integrating a pixel-by-pixel attention mechanism into the backbone network, and suppresses the interference of clothing-related information on the model by learning the identity information of the pedestrian through the body shape branch, so that the model pays attention to deep-level clothing-irrelevant feature information.

The embodiment of the invention belongs to the field of multi-camera systems, and relates to a multi-target area dynamic vehicle and pedestrian recognition method, which comprises the following steps: acquiring image data of a target area acquired by a plurality of cameras; preprocessing the image data, and extracting features of the target area; target detection is carried out on the image data, vehicle and pedestrian targets in the image data are identified, and the positions and features of the targets are determined; performing preliminary positioning on each target, and predicting the motion trail of the target through a Kalman filtering algorithm or a particle filtering algorithm; and carrying out space-time association on the targets identified by the plurality of cameras based on a joint data association method of the positions, the features and the motion trails of the targets. The invention also provides a multi-target area dynamic vehicle and pedestrian identification system. The coverage rate and accuracy of target identification are improved.

According to the clothes changing pedestrian re-identification algorithm based on gating self-adaptive image-text feature fusion, the limitation of a traditional pedestrian re-identification method in facing complex scenes such as clothes changing and background changing of a target person is overcome by combining image and text description information, and by introducing clothes change information from text description, the re-identification accuracy of the clothes changing pedestrian is improved. And through modal fusion of image features, accurate pedestrian identification under different clothing conditions is realized. According to the algorithm, features are extracted through a large model, and adaptive fusion is performed on the features of two modalities in combination with an image feature attention module attI, a text feature attention module attT, a weight calculation dynamicscale module and a gating adaptive feature fusion module. Meanwhile, the performance of the algorithm is effectively improved by introducing a contrast learning calculation loss function.

The invention provides a visible light infrared pedestrian re-identification method based on text assistance. The method comprises the following steps: acquiring an infrared pedestrian image to be retrieved and a visible light pedestrian image library, and labeling rough text description on the infrared pedestrian image; preprocessing the infrared pedestrian image and the visible light pedestrian image; constructing an asymmetric double-branch cross-modal feature extractor, introducing text description as auxiliary information, and extracting features of a to-be-retrieved pedestrian image and all pedestrian images in an image library; calculating the cosine similarity of the two, sorting the similarities from high to low, and generating an initial retrieval sequence; calculating the similarity between the pedestrian images in the image library, and generating a neighbor set according to the similarity; and calculating neighbor similarity, and reordering the initial retrieval sequence according to the neighbor similarity. According to the method, text description is introduced as cross-modal retrieval auxiliary information, and a cross-modal retrieval reordering algorithm is designed, so that the problem of cross-modal pedestrian feature alignment can be well solved, and a pedestrian re-identification result which is more accurate than that of an existing method is obtained.

The invention discloses a multi-sensor fused intelligent AGV path planning method and system, and the method comprises the steps: enabling an AGV to carry a 360-degree laser radar sensor, a binocular vision sensor, an infrared thermal imaging sensor, an ultrasonic array sensor and other sensors, processing data through a low-rank multi-source feature fusion module LMF, and constructing a high-precision two-dimensional grid map through a VF-Cart algorithm; the global path planning uses an improved MRRT * algorithm, and the algorithm combines the dynamic data processing capability of a Markov decision process MDP and the efficient search capability of RRT * to ensure the path optimality; local path optimization adopts an FPA * algorithm fusing an artificial potential field method APF and a dynamic heuristic search algorithm DHPA *, local optimum is avoided, and intelligent obstacle avoidance is realized in combination with deep reinforcement learning DRL; according to the method, centimeter-level obstacle detection and dynamic pedestrian recognition are realized in a complex dynamic environment, the path planning efficiency and the motion stability of the AGV are remarkably improved, and the scheduling time is shortened.

The application discloses a low-light target detection method based on a double-layer optimization theory, which significantly improves the enhancement quality and detection accuracy. The method first adopts a double-layer learning architecture to bridge the enhancement and detection tasks, and organically combines the low-light image enhancement task and the target detection task. Secondly, the enhancement network parameters are taken as the main optimization target, and the detection network parameters are taken as the constraint condition. The enhancement network generates images that are conducive to machine perception. Finally, in order to improve the training efficiency, a method based on the implicit function theory is proposed to approximately solve the corresponding gradient. The training cost is effectively reduced, and the training speed is improved. The application significantly improves the target detection accuracy in weak light scenes, and can be applied to various target detection tasks in weak light scenes, such as night pedestrian recognition, automatic driving, security monitoring and the like.

The application discloses a kind of based on three modal consistency constraint cross-modal pedestrian re-identification method and system, the method includes: constructing pedestrian identification model;Fusion color and infrared image, produce enhanced sample;Color, infrared and enhanced image input model, extract image feature map;Implementation pooling operation obtains the overall and local features of image, input classifier completes classification task;Construct tri-center loss, reduce the feature difference between modal;Compact modal constraint is built, reduce the feature difference within modal;Joint cross-entropy, tri-center loss and compact modal constraint, optimize pedestrian identification model;Test application stage, the overall and local features of pedestrian are extracted, and it is concatenated to carry out pedestrian matching, and recognition result is obtained.The application makes full use of infrared and color image information, produces enhanced image to reduce the difference between modal, constructs loss to solve the problem that modal and modal difference is big, improves the matching accuracy of cross-modal pedestrian re-identification.

The present disclosure provides a data processing method, device, equipment, storage medium and program product, which relate to technical fields such as artificial intelligence or finance. The data processing method includes: obtaining M images collected by N cameras, wherein the M images include an image of a target user; inputting each of the M images into a pedestrian re-identification model according to the time sequence of collection; using the pedestrian re-identification model to confirm the identity and behavior status of the target user based on a first sequence feature, wherein the pedestrian re-identification model performs feature fusion on the features of each image based on the time series to obtain the first sequence feature. The present disclosure also provides a training method, device, equipment, storage medium and program product.

This application relates to the field of image detection technology, specifically to a method, device, and system for pedestrian trajectory tracking across cameras. Specifically, it includes: dividing a surveillance image into far-field, mid-field, and near-field regions; analyzing the imaging characteristics of moving pedestrians in different regions to construct regional change coefficients for far-field and near-field regions at different time periods; constructing motion change influence coefficients for each region by analyzing the pixel feature changes of clusters within the surveillance image during pedestrian movement and the grayscale changes of each pixel over time; determining the tracking region in each far-field and near-field region based on the regional change coefficients and motion change influence coefficients; reducing the hard threshold for denoising in the tracking region and mid-field region to retain more image details, thereby performing pedestrian trajectory tracking; avoiding the problem that traditional denoising algorithms use the same denoising parameters for the entire image, which may smooth the pedestrian's features during denoising, affecting the accuracy of pedestrian recognition.