129 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 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 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 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 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 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 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 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 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.

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.

The invention relates to a multi-spectral image and point cloud fusion pedestrian identification and safety protection method based on cross-modal feature cooperative constraint, and belongs to the technical field of target identification. The method comprises the following steps: firstly, carrying out space-time synchronization on a point cloud acquired by a laser radar and an image acquired by a multispectral camera, and then preprocessing synchronized point cloud data and image data; in the segmentation stage, two paths of complementary image segmentation paths are adopted to respectively extract pedestrian and dangerous object areas, and two paths of segmentation results are fused and complemented, so that the stability and accuracy of pedestrian identification are improved. And finally, performing morphological optimization on the segmentation mask, fusing with the point cloud data, and realizing calculation of the shortest distance between the pedestrian and the dangerous object in combination with a convex hull grid projection algorithm of spatial index acceleration so as to support personnel safety protection and target early warning. According to the invention, the identification degree of pedestrians and dangerous objects in the image can be improved, and an active safe distance protection function is realized.

The application discloses a kind of identification methods of pedestrian on crosswalk, the method utilizes vehicle front camera to obtain the image corresponding to vehicle front scene, obtains the color block in image and carries out screening, and then obtains the regression straight line of color block, utilizes the characteristics of the longitudinal boundary line of white color block of crosswalk in the image of front camera compared to a point to the regression straight line in image is screened and numbered, and the regression straight line screened is the longitudinal boundary line of white color block of crosswalk.By the change of regression straight line number in detection process, the shielding condition of pedestrian to the white color block of crosswalk is reflected, and then the identification of pedestrian on crosswalk is realized.

The invention relates to the technical field of image data processing, in particular to a vehicle-mounted thermal imaging target AI identification method supporting aerial imaging display. The method comprises the following steps: acquiring a thermal imaging video stream and vehicle speed data shot in a vehicle driving process; identifying each suspected pedestrian area of adjacent image frames in the thermal imaging video stream, and calculating the area similarity of each corresponding suspected pedestrian area of the adjacent image frames; performing picture consistency matching on the adjacent image frames to enable the adjacent image frames to complete image alignment processing; reflecting the change trend of the whole picture by using the vehicle speed data, and obtaining a pedestrian body region and a pedestrian limb region in the aligned picture by combining the region similarity of each suspected pedestrian region in the aligned picture; and determining whether pedestrian features exist in the aligned picture or not, and marking and displaying the pedestrian features when the pedestrian features exist in the corresponding region. Based on the technical scheme of the invention, accurate pedestrian recognition can be realized.

The invention discloses a semi-supervised pedestrian re-identification method based on multistage clustering. The method comprises the following steps: acquiring video data; and inputting the video data into a pedestrian re-identification model to obtain a pedestrian identification result. Wherein the pedestrian re-identification model is obtained according to the following steps: training a feature extraction network by using a labeled training set; for unlabeled unsupervised data, extracting corresponding output features by using the trained feature extraction network; performing single-track clustering, single-video clustering and cross-video clustering on the output features to obtain label information, and further constructing an automatic labeling pedestrian training set; carrying out distillation training on a teacher-student mutual learning framework based on the automatic marking pedestrian training set and supervised data, wherein the teacher-student mutual learning framework comprises a teacher model and a student model; and taking the student model subjected to distillation training as the pedestrian re-identification model. According to the invention, the data annotation cost is reduced, and the generalization ability of the pedestrian recognition model is improved.

The application provides a kind of automobile crane personnel intrusion monitoring method and device, belong to engineering machinery safety monitoring technical field.The method includes: synchronous acquisition contains the RGB image of pedestrian information, disparity map and three-dimensional point cloud data, and time-space registration is carried out and is preprocessed to establish training set;Personnel intrusion monitoring model of automobile crane is constructed, including feature extraction module, boom prediction module, pedestrian position prediction module and time-space registration module;Personnel intrusion monitoring model of automobile crane is trained using training set data;Collect construction site RGB image, disparity map and three-dimensional point cloud data, after time-space registration, input personnel intrusion monitoring model of automobile crane completed training according to the relationship between the position of pedestrian, motion direction and fence, determine intrusion risk.The application improves the pedestrian recognition accuracy in complex environments such as rain, fog and occlusion through the time-space attention feature fusion mechanism, and can provide early warning for potential intrusion behavior.

The invention relates to a method for controlling a driver assistance system in a road vehicle (22) having an electronically controllable drive motor (24) and an electronically controllable service brake system having wheel brake cylinders (30, 32, 34, 36) which can be actuated with pressure medium, and having a camera-based vehicle recognition, pedestrian recognition and traffic sign recognition, as well as a speed regulation function and an emergency brake function, wherein, when the road vehicle (22) approaches an identified bus stop or tram stop (2), the controller (26) of the driver assistance system, in addition to generating and outputting a warning signal to the driver of the road vehicle (22), also carries out at least one further measure to avoid a collision with a pedestrian (14.1, 14.2, 14.3). As an additional measure, according to the invention, the wheel brake cylinders (30, 32, 34, 36) of the service brake system of the road vehicle (22) are pre-filled with a predetermined fluid pressure before reaching the bus stop or tram stop (2) in order to prepare for a possible emergency brake. The invention also relates to a device (40) for carrying out the method.

The present invention provides a method and device for pedestrian recognition. The method comprises: after determining that the tracking identifier trackID of a target person is lost during the tracking process of a frame image in a target video, obtaining a predicted trajectory of the target person based on a target candidate sequence and a historical position sequence; and determining whether the target person is occluded or has left a boundary; performing different processing on the target candidate sequence based on different states of the target person; and identifying the target person using a REID algorithm and face recognition based on the target candidate sequence during the period when the trackID is lost. The present invention processes the target candidate sequence by predicting the pedestrian's motion trajectory during the period when the pedestrian tracking identifier trackID is lost in the video frame image, thereby improving the stability and continuity of target tracking in the occluded state. In the state when the pedestrian has left a boundary, video splicing technology is used to verify the correctness of the pedestrian's motion trajectory, thereby improving the accuracy of pedestrian recognition.

The application discloses a light neural network model edge end cross identification vehicle courtesy pedestrian method and system, belongs to the technical field of identifying vehicle courtesy pedestrians, and comprises the following steps: S1: a vehicle-mounted road surface video collector of a bus video acquisition system collects three groups of picture data of no zebra crossing picture D1, no pedestrian on the zebra crossing D2 and pedestrian on the zebra crossing D3 in a driving process; S2: the collected picture data is subjected to image preprocessing; S3: a zebra crossing identification model is trained by using negative samples (D2+D3) and positive samples D1, and a pedestrian identification model on the zebra crossing is trained by using positive samples D3 and negative samples D2; and S4: when a camera in the vehicle does not detect a zebra crossing on the road, only the zebra crossing identification model is kept listening, and when the zebra crossing on the road is detected; listening data is selectively collected to consider a multidimensional interaction state of a vehicle and a target traffic light area road condition, and a vehicle courtesy pedestrian behavior is quantified in a scoring mode.

This disclosure relates to the field of pedestrian recognition technology, and provides a method and apparatus for identifying people traveling together. The method includes: constructing an encoder using multiple fully connected layers; constructing a self-optimizing clustering module based on a clustering algorithm; constructing a people traveling together recognition model using a graph attention network, the encoder, and the self-optimizing clustering module; training the people traveling together recognition model using a reconstruction loss function, a mapping loss function, and a clustering loss; acquiring pedestrian images; extracting pedestrian relationship features and pedestrian attribute features from the pedestrian images; inputting the pedestrian relationship features and pedestrian attribute features into the trained people traveling together recognition model; and outputting the recognition result of whether the pedestrian in the pedestrian image is a person traveling together. By employing the above technical means, the problem of high computational load and low efficiency in existing people traveling together recognition methods is solved.

The invention relates to the field of traffic video data processing, and more specifically relates to a traffic video data processing method and system based on artificial intelligence, a data acquisition module is adopted, the data acquisition module obtains traffic video data recorded by a monitoring module, the monitoring module comprises a monitoring camera, and the monitoring camera is connected with the data acquisition module. The geomagnetic sensor and the meteorological sensor acquire vehicle passing information, and the result output and application module receives processed traffic data, generates a traffic report and provides decision support for an intelligent traffic system. The data acquisition module acquires mass traffic video data, the data preprocessing module performs format standardization, noise removal and image enhancement processing on the data, the problem of processing errors caused by old data is avoided, in addition, the target detection and recognition module recognizes the color of a vehicle based on a deep learning computing platform, and the accuracy of the vehicle color recognition is improved. Identifying the posture of the pedestrian; and the content of the traffic sign is accurately judged, so that the effect of accurate analysis is achieved.

The application relates to a pedestrian attribute recognition method based on deep learning and belongs to the technical field of pedestrian recognition. The recognition method comprises the following steps: constructing a pedestrian attribute recognition network; the pedestrian attribute recognition network comprises a backbone network, a first pooling layer and a full connection layer, and an attention module is introduced between the backbone network and the first pooling layer; training the pedestrian attribute recognition network; and performing pedestrian attribute recognition according to the trained pedestrian attribute recognition network. The pedestrian attribute recognition network for performing pedestrian attribute recognition introduces an attention module between the backbone network and the first pooling layer, the importance of high-level semantic information is distinguished through the attention module, and then the high-level semantic information is purposefully selected and processed by the first pooling layer, the global features corresponding to the high-level semantic information are emphasized, and the recognition accuracy is improved. The pedestrian attribute recognition network structure is simple, the complexity of the network structure is avoided from being greatly increased, and the recognition efficiency is improved.

The embodiment of the application belongs to the field of multi-camera systems, and relates to a multi-target region dynamic vehicle and pedestrian identification method, which comprises the following steps: acquiring image data of a target region collected by multiple cameras; pre-processing the image data to extract features of the target region; performing target detection on the image data to identify vehicle and pedestrian targets in the image data and determine positions and features of the targets; preliminarily positioning each target and predicting a motion trajectory of the target through a Kalman filtering algorithm or a particle filtering algorithm; and performing space-time correlation on the targets identified by the multiple cameras based on a joint data correlation method of the positions, features and motion trajectories of the targets. The application also provides a multi-target region dynamic vehicle and pedestrian identification system. The application improves the coverage and accuracy of target identification.

The invention discloses a vehicle and pedestrian identification method, device and system, and the method comprises the steps: transmitting a control instruction to each signal transmitter, enabling each signal transmitter to transmit a respective detection signal according to the control instruction, and enabling a pedestrian terminal device to generate a feedback signal according to at least one detection signal after receiving the at least one detection signal; acquiring a feedback signal, and performing pedestrian attribute recognition and pedestrian position recognition according to the feedback signal to obtain a recognition result; and determining whether there is a collision risk between the vehicle and the pedestrian according to the identification result, and if yes, controlling the vehicle to execute an anti-collision measure. According to the invention, a parallel pedestrian recognition scheme can be provided when the existing pedestrian recognition scheme of the vehicle fails or even fails, and the pedestrian recognition accuracy of the vehicle is ensured.

The invention discloses a lightweight live-action pedestrian recognition method based on an attention mechanism and shared convolution, and the method comprises the following steps: obtaining a network model used for a live-action pedestrian target recognition task in a dynamic traffic scene, namely, a basic model of YOLOv8; the model is improved, and a special detection model is designed for a live-action pedestrian target recognition task in a dynamic traffic scene; a structured pruning strategy is used for implementing parameter simplification on the obtained model, and the calculation efficiency is improved; and the model is embedded into a platform with a photo receiving function, so that real-time detection can be carried out. The method has high application value and academic value in the pedestrian detection field, various technical means are used for improving a related network model, compared with a mainstream scheme, the improved model effectively solves the contradiction between light weight and real-time performance of pedestrian detection in a complex traffic scene, and the method is suitable for popularization and application. Reliable technical support is provided for practical application in the fields of intelligent traffic, automatic driving and the like.