60 results about "Pedestrian recognition" patented technology

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 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 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 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 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.

A pedestrian detection device for blind spots on buses with risk avoidance function includes a powered telescopic frame, a pedestrian recognition module, an audible and visual alarm, an environmental recognition module, a geographic location recognition module, an emergency avoidance module, and an embedded microcontroller. The powered telescopic frame is installed on the front roof of the bus. The pedestrian recognition module, environmental recognition module, and emergency avoidance module are all mounted on the powered telescopic frame. The audible and visual alarm and the embedded microcontroller are both installed on the interior ceiling of the bus, directly below the powered telescopic frame. The geographic location recognition module is mounted on the embedded microcontroller. The embedded microcontroller is connected to the powered telescopic frame, pedestrian recognition module, audible and visual alarm, environmental recognition module, geographic location recognition module, and emergency avoidance module via signals. This invention can effectively identify pedestrians in the blind spots of buses, alert the driver to driving safety through the audible and visual alarm to avoid traffic accidents, and also has risk avoidance and emergency avoidance functions.

The invention relates to an intelligent control method and system for an electric sliding door, and relates to the technical field of intelligent doors and windows, and the method comprises the steps: obtaining an infrared detection state of a detection area in front of a door; acquiring an infrared identification position when the infrared detection state is consistent with the effective identification state, and calling a preset image shooting device according to the infrared identification position to acquire a region detection image; pixel points corresponding to the infrared identification positions are defined as pedestrian identification points in the area detection image; comparing the area detection image with a preset original image in front of a door to determine distinguishing feature points; when the distinguishing feature points are adjacent to the pedestrian recognition points, the distinguishing feature points are automatically updated to the pedestrian recognition points, and after all the distinguishing feature points are analyzed, the pedestrian passing width is determined according to all the pedestrian recognition points; and according to the pedestrian passing width and the preset surplus width, the reasonable opening width is determined through calculation, and the sliding door is controlled to be movably opened according to the reasonable opening width. The application has the effect of facilitating the use of the sliding door.

The application provides a pedestrian recognition method, system and device based on radio frequency waves and a storage medium. The method comprises the following steps: acquiring a radio frequency thermal map group in a target area, acquiring pedestrian image data captured in the target area according to a preset capturing condition, and extracting pedestrian capturing information from the pedestrian image data; mapping the pedestrian capturing information to the radio frequency thermal map group to cut out a pedestrian radio frequency thermal image group from the radio frequency thermal map group; extracting pedestrian features from the pedestrian radio frequency thermal image group; and comparing the extracted pedestrian features with pedestrian features stored in a database to identify whether the captured pedestrian is a warehouse entry personnel. The method based on RF-PersonReID can effectively manage warehouse entry and exit personnel, complete the statistics of warehouse entry and exit personnel information and warehouse residence time, and effectively solve the visual interference caused by personal dress change or the same dress.

The invention relates to the technical field of traffic control, in particular to an intersection traffic control method and system based on vulnerable group passage optimization. When the traffic light is in a green light state, vehicles pass, pedestrians wait at the two sides of the pedestrian path, the identification module obtains human body characteristics and modes of the pedestrians at the two sides of the path at the moment to identify whether vulnerable pedestrians exist or not, and if the vulnerable pedestrians exist, the identification module marks the vulnerable pedestrians as first vulnerable pedestrians and determines vulnerability labels and levels of the vulnerable pedestrians; and the control module determines a delay coefficient according to the delay coefficient, and prolongs the duration of the traffic light in the red light state next time according to the delay coefficient, so that the first vulnerable pedestrian has more passing duration. Besides, in order to avoid corresponding traffic flow congestion caused by red light delay, cloud and dynamic adjustment can be carried out on flow dredging of the original traffic flow based on flow dredging compensation delay increment of the downstream intersection, a more humanized traffic environment can be constructed in an aging structure, and the method has remarkable social benefits.

A method is provided for determining correctives to statistical identification variances of a biometric identification system using facial and / or pedestrian recognition. The method includes (a) defining a set of categories, each category having at least one corresponding physical appearance characteristic; (b) extracting, for each individual, at least one physical appearance characteristic from at least one image of the individual; (c) assigning, to each individual, at least one category on the basis of the extracted physical appearance characteristic; (d) distributing, for each category, each individual into two groups according to the failure or success of their identification by the system; and (e) calculating, for each category, a number of individuals to be selected in the group corresponding to the success of the identification such that the relative proportions of individuals in the group corresponding to the failure of the identification are substantially equal between all the criteria relating to said category.

The invention relates to the technical field of image recognition, in particular to a pedestrian target recognition method and system based on rapid image enhancement, and the method comprises the following steps: obtaining a pedestrian video; performing rapid enhancement processing on image frames in the pedestrian video to obtain an enhanced pedestrian video; a high-frame-rate pedestrian recognition model is formed by combining a YOLOv5 algorithm, a DeepSORT algorithm and a CBAM attention mechanism, and zoom intersection-union ratio loss is introduced for training; and performing pedestrian identification and tracking on the enhanced pedestrian video through a high-frame-rate pedestrian identification model. According to the method, the image frame detection and enhancement model is integrated as an image preprocessing mechanism, and the improved high-frame-rate pedestrian recognition model is combined, so that the algorithm structure is optimized, the recognition performance is improved from the data source, and efficient and accurate pedestrian multi-target recognition is realized.

The invention relates to the technical field of blind area pedestrian recognition, in particular to an industrial equipment blind area pedestrian recognition and grading early warning method based on AI vision. The method comprises the following steps that a plurality of joint points of the mechanical arm are obtained and segmented into mechanical arm points, and then camera shooting equipment is triggered to obtain surrounding environment image data; pedestrians are recognized according to the surrounding environment image data, the shortest distances between the pedestrians and boundary line segments of the left side, the right side and the rear side of the rectangular range area are calculated, then historical data are obtained, different track route areas of the pedestrians are predicted, the different track route areas of the pedestrians and the rectangular range area of the operation track of the mechanical arm are intersected, and blind area areas are obtained. And then the height value from the point of the mechanical arm to the ground is calculated, it is judged that the pedestrian selects to pass through the blind area below the mechanical arm or bypass the blind area behind the mechanical arm, more calculation resources and monitoring attention can be focused on the blind area where the pedestrian possibly passes through actually through different track route areas of the pedestrian, and the whole monitoring efficiency is improved.

The present technology relates to a recognition processing device, a recognition processing method, and a recognition processing system capable of realizing more efficient object recognition.The dictionary data storage unit stores a far dictionary for each pattern to be recognized and a nearby dictionary for each pattern, and the recognition processing unit executes recognition processing of recognizing an object included in a wide-angle image captured by a wide-angle camera using the far dictionary and the nearby dictionary. Further, the recognition processing unit recognizes the object by using a plurality of patterns of the far dictionaries and a nearby dictionary of the same pattern as the far dictionary of a high likelihood pattern in which the object is recognized with high likelihood among the far dictionaries. The present technology can be applied to, for example, an image recognition chip that performs pedestrian recognition on a wide-angle image.

The invention provides a vehicle pedestrian detection method and system, equipment and a medium, and belongs to the technical field of automobile automatic driving, and the method comprises the steps: obtaining a to-be-detected image; the to-be-detected image is input to a pre-trained vehicle and pedestrian detection model, the vehicle and pedestrian detection model outputs a vehicle and pedestrian description feature map corresponding to the to-be-detected image, and the vehicle and pedestrian detection model adopts a YOLOv7 model and a convolution block attention module to perform feature extraction and feature fusion; and decoding the vehicle and pedestrian description feature map to obtain a vehicle and pedestrian recognition result. Based on the YOLOv7 neural network model, the attention module suitable for vehicle and pedestrian detection in the complex scene is added to the feature fusion part of the original model, and the SOA data enhancement technology and the weighted image training strategy are used, so that the model can deeply understand and process the complex traffic scene, and the accuracy of vehicle and pedestrian detection is improved.

The application provides a human body photo dynamic storage method and system for pedestrian re-identification, which comprises: when a certain pedestrian S enters the collection area of a first camera P1 deployed in a monitoring scene, pedestrian identification and human body ID tracking are performed, and human body photos are continuously captured to form a human body photo sequence of the pedestrian S in the collection area of the first camera P1; the human body photo sequence and the human body coordinate values corresponding to the photos are summarized and uploaded; the server platform determines the moving track of the pedestrian S according to the human body coordinate values corresponding to the human body photos, calculates the human body moving speed and direction, and obtains the nearest two camera points and the nearest expected arrival time point of the pedestrian S according to the nearest two camera points; the human body number in the human body database is updated or the human body database is maintained unchanged by combining the comparison of the characteristic values and whether the time error of the human body passing through the adjacent two cameras is within the allowed range, so as to reduce the size of the human body dynamic database and improve the efficiency and accuracy of cross-camera identification.

The present application relates to the technical field of computer vision and cross-view pedestrian recognition, and particularly relates to a cross-view pedestrian re-identification method based on visual language prompt learning, comprising: acquiring a target image; inputting the target image into a preset pedestrian recognition model to output a pedestrian re-identification result, wherein 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, wherein the prompt learning mechanism is used for modeling view bias, and the two-stage training strategy is used for realizing cross-modal semantic alignment. The present application can significantly improve the accuracy and robustness of cross-view recognition.

The application belongs to the field of artificial intelligence, and particularly relates to a lightweight pedestrian dynamic feature extraction and recognition method and system based on video motion vectors. By constructing a motion decoupling and differential coding framework, the efficient pedestrian target capture and real-time low-delay recognition problem in complex motion scenarios is effectively solved. Specifically, the DiVE framework utilizes the spatiotemporal characteristics of motion vectors to accurately extract pedestrian dynamic features in dynamic environments, and reduces the computational and transmission bandwidth requirements through differential coding technology, thereby optimizing the processing performance of edge devices. This method not only improves the accuracy of pedestrian recognition, but also significantly reduces the power consumption and delay of the system, and has a wide application prospect, especially in intelligent video surveillance, autonomous driving and smart city fields.

The invention discloses a pedestrian re-identification method and device, equipment and a medium, and relates to the technical field of computer vision, and the method comprises the steps: collecting a shielding object image with an irregular edge, setting a position and size constraint rule of a shielding object, converting the shielding object to be attached to the surrounding of a pedestrian in a pedestrian target image, and carrying out the reconstruction of the shielding object. Therefore, the shielding distribution in a real scene is effectively simulated; generating a pixel-level shielding mask of the simulated shielding image, converting the shielding mask into a weight vector, performing element-by-element weighting on the attention weight matrix by using the weight vector so as to attenuate the attention weight of the shielding area, and applying negative offset to the attenuated attention weight element by element so as to obtain the attention weight of the shielding area. The process is based on a soft and hard dynamic suppression mechanism guided by a mask, attention weight distribution is dynamically adjusted through self-adaptive interaction, dynamic constraint on attention weights from a pixel-level space angle is achieved, and therefore the recognition effect on pedestrians in a complex shielding scene is improved.

The invention relates to the technical field of image detection, in particular to a cross-camera pedestrian trajectory tracking method, device and system, and the method specifically comprises the steps: carrying out the long-shot, medium-shot and close-shot region division of a monitoring image, analyzing the imaging features of a moving pedestrian in different regions, and respectively constructing the region change coefficients of the long-shot and close-shot regions in each time period; constructing a movement change influence coefficient of each region through pixel point feature change of a clustering cluster in a monitoring image and gray scale change of each pixel point along with time in a pedestrian movement process; on the basis of the region change coefficient and the movement change influence coefficient, determining tracking regions in each long-shot region and each close-shot region, reducing hard thresholds during denoising for the tracking regions and the middle-shot region to reserve more image details, and then performing pedestrian trajectory tracking; the problem that the accuracy of pedestrian recognition is affected due to the fact that the same denoising parameters are used for the whole image in a traditional denoising algorithm and the features of pedestrians are possibly smoothed while denoising is carried out is solved.

The invention discloses an infrared pedestrian recognition model and recognition method based on an attention module. The model comprises a backbone network, a feature fusion network and a detection head network, an SE channel attention module is introduced behind a pooling module of the backbone network, and a SimAM space attention module is introduced before data enters the feature fusion network. The SE module is used for realizing feature weighting and re-calibration in a channel dimension, and the SimAM module is used for enhancing pedestrian target features and weakening background interference in a space dimension. The model is suitable for infrared pedestrian recognition of an embedded platform, and high-precision detection and real-time recognition of a pedestrian target in a low-resolution infrared image are achieved.

The application discloses a pedestrian re-identification method and device, equipment and medium, and relates to the technical field of pedestrian re-identification. The method comprises the following steps: obtaining a to-be-identified global feature map and an original global feature map based on a to-be-identified pedestrian image and a plurality of original pedestrian images; obtaining a weighting weight coefficient corresponding to the to-be-identified global feature map and the original global feature map respectively, obtaining a weighted to-be-identified global feature map and a weighted original global feature map based on the corresponding weighting weight coefficients; obtaining pedestrian attribute features corresponding to the weighted to-be-identified global feature map and the weighted original global feature map respectively and pedestrian global features corresponding to the weighted to-be-identified global feature map and the weighted original global feature map respectively, obtaining pedestrian identification features corresponding to the to-be-identified pedestrian image and the original pedestrian image respectively based on the pedestrian global features and the pedestrian attribute features; and extracting a target pedestrian image corresponding to the to-be-identified pedestrian image from the original pedestrian image based on the pedestrian identification features to realize pedestrian re-identification. The application improves the accuracy of pedestrian re-identification.