274 results about "Person detection" patented technology

Disclosed in the present application is a skeleton detection and fall detection method based on improved spatio-temporal adaptive graph convolution. The method comprises the steps of: S1, collecting image data to acquire data of each image frame; S2: using a pre-trained yolov5 target person detection model to detect whether a target person is present in the data of each image frame, and if a target person is present, turning to step S3, and if no target person is present, ending the process; S3: for each detected target person, using a Deepsort target tracking algorithm to perform target tracking to obtain a tracking result, calculating the similarity to obtain the result of target association, and updating trajectory information of each target person; and S4: performing pose recognition on each target person on the basis of the trajectory information, using a spatio-temporal adaptive graph convolutional network to extract a feature vector of a pose, and using a classifier to perform human body behavior classification and recognition, in order to determine whether the target person has experienced a fall incident. The method achieves higher accuracy and robustness.

The invention discloses an interactive action detection method based on multi-level features, and the method comprises the steps: S1, fusing the information of a low-level feature map, a middle-level feature map and a high-level feature map through a cascading fusion mode, and obtaining a fused feature map; s2, acquiring local detail features from the fused feature map, performing global modeling to obtain global features, fusing the global features with the local detail features to generate global context features, performing character detection, object detection and interaction detection in parallel through multi-task branches, outputting character features, object features and interaction features, and outputting the character features, the object features and the interaction features. S3, gradually fusing multi-level contexts through attention interaction of a unitary relationship, a pairwise relationship and a ternary relationship, generating text embedding for an interaction category by utilizing a pre-training text encoder, and aligning the text embedding with visual features to improve the accuracy of fine-grained interaction classification; and S4, explicitly modeling a complex interaction relationship, and finally predicting and outputting a final prediction result by using FFN.

The invention relates to the technical field of artificial intelligence computer vision, in particular to an event camera pedestrian detection method based on a space-time state space model, and the method comprises the steps: collecting a pedestrian detection data set based on an event camera, obtaining original event data, carrying out the preprocessing, determining an event tensor, and obtaining an event frame sequence; modeling is carried out through combination of a state space and an event tensor, an event-driven recursive space-time state space module is defined as a core unit, and an isomorphic deep neural network architecture is constructed; training the isomorphic deep neural network architecture by adopting the training set, and verifying through the verification set; inputting the test set into the verified isomorphic deep neural network architecture for detection, and generating a pedestrian detection result; the collaborative optimization of sparse adaptation-dynamic capture-noise suppression is realized in a unified framework, the essential characteristics of the event camera triggered based on brightness change are theoretically fit, and higher robustness and generalization ability are shown in an actual complex scene.

The invention discloses a multi-view child motion coordination ability evaluation system and method based on deep learning, belongs to the technical field of motion evaluation, and solves the problems that child motion evaluation in the prior art mainly depends on manual observation and simple physical testing, and multiple angles and key details of child actions are difficult to synchronously track. The method comprises the following steps: training to obtain a skeleton point detection model and an athletic ability evaluation model, acquiring real-time video streams of personnel entering a field based on an acquisition camera, identifying skeleton key points in a preprocessing data set by the skeleton point detection model, performing multi-person detection on a personnel matching result based on an athletic area division method, and evaluating the athletic ability of the personnel. The exercise ability evaluation model carries out quantitative analysis on multi-person detection results; according to the invention, visual identification and motion state detection technologies are combined, a front view angle and side view angle dual-camera layout is adopted, and a deep learning algorithm is matched, so that automatic children dynamic motion evaluation is realized. The action process can be completely captured, and the detection accuracy and efficiency are improved.

The invention relates to a target person detection method and device based on millimeter wave radar, equipment and a medium. According to the method, radar detection configuration is adjusted, a gridding coordinate system is established in a target treadmill monitoring area, millimeter wave radar is used for collecting feature data related to movement of a target person, and movement track changes of the target person are extracted to generate person position information; acquiring breathing and heartbeat signals of a target person, and performing signal filtering and feature extraction to obtain vital sign information; comprehensively evaluating the existence state of the target person according to a preset threshold value and a discrimination standard in combination with the person position information and the vital sign information; and when the evaluation result shows that the target person leaves, the vital signs are abnormal or a cheating behavior exists, judging that the target person is in an abnormal state, and generating a corresponding early warning instruction. By adopting the method, abnormal conditions can be found in time, early warning can be generated, and the monitoring accuracy and response speed are improved.

The invention relates to a dense pedestrian detection method for a traffic scene, and belongs to the technical field of machine vision, and the method comprises the following steps: S1, collecting pedestrian images of the traffic scene, and constructing a training and testing data set; s2, constructing a pedestrian detection network, fusing high and low frequency attention and a bidirectional feature fusion module, and introducing a target frame matching strategy; s3, preprocessing the image, and inputting the preprocessed image into a detection network training model; and S4, deploying the trained model to a traffic monitoring system to realize pedestrian detection. The method improves the detection precision and the calculation efficiency, still has the precise detection capability in a complex background and a dense shielding environment, and can be applied to an intelligent traffic monitoring and automatic driving system.

The invention discloses a multi-pedestrian target detection method based on a YOLOCDG network in a complex scene, and aims to solve the problems of small target missing detection, model redundancy and inaccurate positioning in multi-pedestrian detection in a scene with dense people flow and serious shielding. The method comprises the following steps: firstly, based on YOLOv8, deleting a redundant convolution fusion layer of a backbone network, adjusting the size of a detection head, and simplifying a framework to reduce small target feature dilution; secondly, designing a context attention module CoAM and embedding the CoAM into a backbone network, and enhancing feature distinction degree in a shielding scene by capturing cross-target context association of dense pedestrians; thirdly, an improved C2fG module is proposed to replace a neck C2f module, the parameter quantity is reduced, and the detection performance and the edge deployment efficiency in a complex scene are balanced; then, an SPPFDSC multi-scale fusion layer containing depth separable convolution is designed, an original SPPF layer is replaced, and fine-grained feature perception of small-size pedestrians in the distance is enhanced; and finally, optimizing a bounding box loss function by adopting WIOU v3, improving the target positioning precision in the dense people stream, and finally constructing a multi-pedestrian detection model.

The invention relates to the technical field of image processing and target tracking, in particular to a cross-view fusion multi-person detection and tracking method for outdoor view variety, which comprises the following steps of: reconstructing a multi-view picture into three-dimensional / BEV space priori under a unified world coordinate, and feeding back the priori to each camera position feature by using space enhancement attention to obtain a consistent and steady multi-person detection result; then, carrying out online iteration updating on the three-dimensional position and posture of the figure in a tracking mode, introducing OKS-3D gating to combine the 2D key point consistency with the BEV spatial distance, and keeping a uniform ID and a continuous track across cameras; the method solves the problem that the prior art cannot effectively meet the requirements of real-time, accurate and stable target detection, tracking and identity association of an outdoor variety in a complex shooting environment, realizes time-space structured output of accurate detection, stable tracking, unified ID and visual positioning, can directly serve for director scheduling and post-production, and is high in practicability. And the overall manufacturing level and quality of the exterior variety are improved.

The invention provides a target detection method and system and a storage medium, and relates to the field of object detection, and the method comprises the steps: obtaining image data and laser radar scanning data corresponding to a current scene; calling a first pedestrian detection model based on an image to carry out pedestrian detection on the image data to obtain position information of a pedestrian target; calling a second pedestrian detection model based on point cloud data to carry out pedestrian detection on the laser radar scanning data to obtain pose information of a pedestrian target; and calling an external parameter matrix to match the position information and the pose information, and outputting three-dimensional position information of the pedestrian target in a space coordinate system. According to the invention, the detection and positioning of the pedestrian target can be realized without using a high-cost depth camera and a three-dimensional laser radar, and the detection precision of the pedestrian target is improved through two different modal data.

The invention discloses a long-distance identity recognition method for a large area, which belongs to the technical field of computer vision and comprises the following steps: S1, initializing a system; s2, collecting video data; s3, pedestrian detection and segmentation; s4, performing gait recognition; s5, multi-target cross-camera tracking is carried out; and S6, result output and application service. According to the invention, an effective solution is provided for large-scene video intelligent security and protection in a complex environment of a park, the accuracy and traceability of personnel identity recognition in a high-risk area are ensured, and the safety production level of the park is improved; a long-distance multi-target identity recognition scheme is provided, real-time monitoring and early warning of illegal person invasion in key protection areas are achieved, and reliable safety guarantee is provided for public places.

The invention provides a personnel detection method, equipment and a program product, personnel are detected based on a top-mounted camera, and the method comprises the following steps: a target detection network structure receives a plurality of continuous video frames; for each frame, the target detection network structure identifies whether the target person has a predetermined label pattern in a predetermined body area, and counts the total number of frames having the predetermined label pattern; if the total frame number is greater than a preset number threshold value, determining that the target person is a person meeting a preset attribute; otherwise, determining that the target person is a person which does not meet the predetermined attribute; the number threshold is less than a predetermined number and greater than or equal to 2; wherein the target detection network structure is an improved network structure based on YOLO-V5; wherein the YOLOv8 network structure C2f is used for replacing C3 in the YOLO-V5 network structure, the SSPF of the YOLO-V5 network structure is improved into the SSPFM, and the CBAM module of the YOLO-V5 network structure is improved into the CBAMLSA. By means of the technical scheme, the personnel meeting the preset attributes can be accurately recognized in the scene of the top-mounted camera.

The invention discloses a light-weight unmanned forklift AI visual anti-collision method based on a domestic embedded platform, and the method comprises the following steps: S1, carrying out the preprocessing of an RGB image through a light-weight pedestrian detection module, carrying out the automatic pruning and compression of a deep convolutional network based on an image recognition algorithm of a light-weight convolutional neural network, and carrying out the recognition of the deep convolutional network; lightweight pedestrian detection features are extracted; s2, using a lightweight pedestrian distance estimation module to extract lightweight pedestrian distance estimation features by model compression through camera calibration, image correction, stereo matching and distance acquisition; s3, performing feature fusion, performing feature analysis in combination with a dynamic adaptive sparse transformation network, and completing multi-pedestrian detection and pedestrian distance estimation by introducing a sparse feature adaptive reconstruction mechanism; and S4, deployment is carried out on a domestic embedded platform, and software function module cutting is carried out. According to the invention, a lightweight deep learning algorithm and adaptive feature fusion are adopted, and multi-pedestrian anti-collision detection on a domestic embedded platform is realized.

The invention relates to the technical field of computers, and provides a cloud-edge collaborative abnormal behavior character recognition method, device and system, and the method comprises the steps: edge end equipment employs a trained pedestrian detection model to cut out a video sequence containing pedestrians from a received video sequence, and uploads the video sequence containing the pedestrians to a cloud server; the cloud server cuts a video sequence containing pedestrians according to a set time length to obtain video slices, the video slices and prompt words are input into a trained multi-mode large model to obtain an identification result, the prompt words are described in a video question and answer mode, option question and answer pairs in video question and answer are constructed according to related task types, and the identification result is obtained. The options in the video questions and answers are possible behavior types. The lightweight pedestrian detection is integrated on the side end equipment to reduce the transmission, reduce the processing data volume of the cloud server, solve the problem of high demand of cloud computing resources, and improve the efficiency and response speed of the system.

The various embodiments described herein include methods, devices, and systems for providing event alerts. In one aspect, a method includes obtaining a video feed. A frame of the video feed is analyzed at a first resolution to determine whether the frame includes a potential instance of a person. In accordance with the determination that the image includes the potential instance, a region is denoted around the potential instance. The region is analyzed at a second resolution, greater than the first resolution. In accordance with a determination that the region includes the instance of the person. a determination that the frame includes the person is made. An indication of the determination is stored for use in subsequent alert notification processing.

The invention discloses a super-realistic digital human detection method based on motion modeling, and the method comprises the steps: carrying out the motion sequence extraction of a designated person based on a human body three-dimensional model; extracting motion features of the specified character based on the motion sequence and a visual model; and for the extracted motion features, mapping the motion features into a feature space, constructing an action feature distribution hybrid model, calculating an abnormal score of the digital human under normal feature distribution based on the model, and completing digital human counterfeiting detection according to the abnormal score. According to the method and the system, a super-realistic digital human forgery detection method based on action modeling is matched with an existing action capture technology, more universal action characteristics can be learned, so that the effect of detecting the super-realistic digital human is achieved, and the method and the system have relatively high generalization.

The invention relates to the technical field of robot detection, and discloses a Delta mechanical arm precision detection method, system and device and a storage medium, and the Delta mechanical arm precision detection method comprises the steps that multiple actual position coordinates of the tail end of a mechanical arm in the movement process along a preset track are obtained according to a selected detection mode; geometric fitting is carried out based on the actual position coordinates, and corresponding circle center coordinates are determined; the tail end is controlled to move to the circle center coordinates, and actual position coordinates at the moment are obtained; and error information representing an absolute precision error is generated based on a difference value between the position and the circle center coordinate. According to the method, automatic detection of the absolute positioning precision of the manipulator is achieved, and error quantization can be completed without depending on external measuring equipment; the generated error vector accurately reflects the direction and size of system deviation, compensation and correction are facilitated, and the method has the advantages of being high in detection efficiency, reliable in result and high in applicability.

The invention provides a multi-view pedestrian detection method and system based on dual adaptive feature fusion, and belongs to the technical field of computer vision. According to the method, in a unified aerial view space, firstly, a cross-view feature selection module is introduced, and a multi-head self-attention mechanism is utilized to perform dynamic complementation and weighted fusion on features from different cameras so as to enhance the semantic integrity of a shielded area; then, a view channel map attention module is introduced, fused features are constructed into a view-channel map structure, modeling is carried out on the joint dependency relationship between view channels through a map attention perception multilayer perceptron combining global and local context pooling, self-adaptive reweighting of channel importance is achieved, and the self-adaptive reweighting of channel importance is realized; therefore, the discrimination of the fusion features is improved. And the finally obtained unified features are used for ground pedestrian positioning. According to the method, the detection performance bottleneck caused by visual angle shielding and channel redundancy is effectively relieved, and the quality of the ground occupancy map is remarkably improved.

The invention discloses a pedestrian multi-target tracking method based on an attention mechanism, and the method comprises the steps: inputting a current frame image into a trained target tracking model: inputting the current frame image into a backbone network for feature extraction, and obtaining pedestrian feature maps of multiple scales; inputting the pedestrian feature maps of multiple scales into a target sensing module for weighted fusion to obtain a fused feature map; inputting the fused feature map into a feature decoupling module for feature decoupling to obtain a first feature map for target detection and a second feature map for Re-ID; respectively inputting the first feature map and the second feature map into a target detection branch and an Re-ID branch to carry out target detection and Re-ID; and the secondary association algorithm realizes association matching of the pedestrian detection frame and the trajectory based on the detection result and the appearance embedding vector to obtain the tracking result of each target. According to the invention, the accuracy and effectiveness of pedestrian multi-target tracking can be improved.

The invention discloses a vehicle violation manned intelligent detection method and system based on a CLIP and D-Fine cascade framework, and the method comprises the steps: collecting a real-time video frame image of a preset traffic monitoring network, processing the real-time video frame image, inputting a monitoring image into a CLIP-ILP model, employing the CLIP-ILP model as a filter, and combining with a Top-K screening strategy, and screening out candidate images; and inputting the candidate image into a constructed context enhanced D-FINE detection model, carrying out positioning and multi-class detection on a preset target, outputting a multi-class detection result, and executing verification of a spatial co-occurrence rule, a license plate position heuristic rule, a size consistency rule and a context consistency rule. And marking the result which does not pass the verification as suspicious or rejecting the result, and outputting the result which passes the verification as an illegal manned detection result to the target terminal. According to the method, the problem of balance between the recall rate and the precision in manned detection can be solved.

The invention discloses a library people flow monitoring method and system based on multi-mode and trajectory fusion. The method comprises the steps of collecting a video stream, generating a weight image by using an attention mechanism to enhance a pedestrian area, extracting a pedestrian frame through YOLOv11 target detection, and positioning a pedestrian subject and separating a background in combination with a segmentation model; feature points and descriptors are extracted through SuperPoint, inter-frame motion parameters are calculated through feature matching, and tracks are predicted and verified by means of Kalman filtering; and finally, associating pedestrian tracks through unique IDs, and dynamically tracking in and out states to count the pedestrian flow. According to the method, the problems of shielding, small target missing detection and track drifting are effectively solved, and high-precision people flow statistics is realized; more accurate and robust pedestrian detection and tracking are realized through multi-modal data fusion and dynamic trajectory fusion; through the schemes of feature point extraction, Kalman filtering and the like, the prediction of pedestrian motion trails is optimized, and the real-time performance and precision of pedestrian flow monitoring are improved.

The invention discloses a coal preparation plant production personnel positioning method and system based on video analysis, and the method comprises the steps: processing a video stream through employing an improved OfficientDet model, and obtaining an initial target personnel detection frame; processing the initial target person detection frame by using an improved FairMOT model and a dynamic motion model to obtain a continuous tracking trajectory of the target person; processing the continuous tracking trajectory of the target person by using a multiple linear regression model, predicting the position of the next frame, and allocating weights by using an entropy weight method to obtain a predicted motion trajectory; and processing the continuous tracking trajectory of the target person by using a ResNet-50 model to obtain a depth feature, performing identity confirmation of the target person based on the feature, and associating the identity with the predicted motion trajectory to complete person positioning. According to the method, tracking errors caused by environmental changes are reduced, and effective application in different environments is ensured.

The invention relates to the technical field of artificial intelligence, and provides an abnormal behavior alarm method, system and device, a medium and a program product, and the method comprises the steps: obtaining target abnormal behavior classification results corresponding to a plurality of continuous frame images; personnel identity tracking is carried out on the person detection frame in each frame of image, and personnel identity information of each person detection frame in each frame of image is determined; performing abnormal behavior analysis based on the person identity information of each person detection box in each frame of image and the target abnormal behavior classification result thereof to obtain an abnormal behavior analysis result; and performing abnormal behavior alarm based on an abnormal behavior analysis result. According to the abnormal behavior alarm method provided by the invention, the consistency judgment of identity and behavior is remarkably improved, the alarm is triggered based on comprehensive analysis, the false alarm rate in a traditional single-frame classification direct alarm mode can be greatly reduced, the scene adaptability and accuracy of alarm decision are enhanced, and the user experience is improved. And a more stable technical support is provided for real-time monitoring and risk early warning in a complex dynamic scene.

The various embodiments described herein include methods, devices, and systems for providing event alerts. In one aspect, a method includes obtaining a video feed. A frame of the video feed is analyzed at a first resolution to determine whether the frame includes a potential instance of a person. In accordance with the determination that the image includes the potential instance, a region is denoted around the potential instance. The region is analyzed at a second resolution, greater than the first resolution. In accordance with a determination that the region includes the instance of the person. a determination that the frame includes the person is made. An indication of the determination is stored for use in subsequent alert notification processing.

It is provided a people detector (1) for detecting when people pass through a doorway. The people detector (1) comprises: a first image source (11); a second image source (12): a processor (60); and a memory (64) storing instructions (67) that, when executed by the processor, cause the people detector (1) to: receive a first stream of images from the first image source (11); determine, based on the first stream of images, a confidence indicator that a single person passes through the doorway; when the confidence indicator indicates confidence, determine that a single person has passed through the doorway; and when the confidence indicator indicates non-confidence, receive a second stream of images from the second image source (12), and determine, based on the second stream of images, how many people has passed through the doorway.

The invention discloses a night pedestrian detection method based on an artificial intelligence technology, and relates to the technical field of target detection, and the method mainly comprises the steps: preparing a night road pedestrian data set in advance, and carrying out the preprocessing; the method comprises the following steps: constructing a night pedestrian detection model NE-RTDETR based on RT-DETR neck enhancement, wherein the model is composed of a backbone network ResNet18, a neck enhancement network and a decoder; the data set is input into a NE-RTDETR model for training, and a trained best weight file is obtained; inputting a to-be-detected night road pedestrian picture into the NE-RTDETR model according to the trained weight to obtain information about whether a pedestrian exists in the night road pedestrian picture or not; and finally outputting a detection result and evaluating model performance. Through the steps, the problems that an existing detection method is insufficient in accuracy, high in missing detection and false detection rate and the like under the conditions of low illumination and fuzzy shielding at night are solved.

The invention provides a multi-level domain adaptive pedestrian detection method and system based on a teacher model, and the method comprises the steps: training a target detector through employing source domain perspective image data with a pedestrian label, and obtaining an initial target detector; constructing a target domain teacher model and a cross-domain student model; and performing multi-level domain adaptive adversarial training by using the cross-domain student model, updating parameters of the target domain teacher model through index moving average of the weight of the cross-domain student model, generating a pseudo label to supervise the multi-level domain adaptive adversarial training of the cross-domain student model, and optimizing the cross-domain student model in combination with a designed loss function. Obtaining a final cross-domain student model; and detecting pedestrians in the overlooking fisheye image to be detected based on the final cross-domain student model. According to the technical scheme of the invention, the requirements of people flow statistics, safety monitoring and people flow guidance on rapid and accurate detection of pedestrians in a scene can be met.

To enable inexpensive and accurate detection of fraudulent acts in which articles are taken out of a store without being settled after registration.SOLUTION: A fraud detection device includes a person detection unit, an article detection unit, and a fraud reporting unit. The person detection unit detects persons in a first area around a target device based on an image of the target device, which is the subject of the fraud detection. The article detection unit detects at least one of the articles in a second area around the target device and a container containing the articles based on the image. The fraud reporting unit reports fraud when the person detection unit does not detect any person and the article detection unit does not detect either the articles or the container while the settlement process is being executed for the articles.SELECTED DRAWING: Figure 9

The invention relates to a method for improving the detection of remotely controlled dummies (10) for the development and validation of ADAS of a vehicle, wherein sensor data from at least one sensor device are used to identify the dummies (10) based on visual features and / or positions and / or movements, and wherein the sensor data are processed using image processing and / or sensor data fusion techniques to detect the dummies, wherein, depending on the detected dummies (10), appropriate measures for vehicle control are initiated by the ADAS, characterized in that the sensor data are linked with artificially realistic objects of the detected dummies (10) using existing video processing methods with a corresponding algorithm and the measures for vehicle control are adapted accordingly.Furthermore, a system, a computer program product, and a non-volatile, computer-readable storage medium.

The invention provides an image detection method and an image detection system. The image detection method comprises the following steps: obtaining a detection image through a camera; calculating the width and the height of a view field range corresponding to the detection image according to the horizontal view field angle and the vertical view field angle of the camera through the processor; calculating, by the processor, the width per pixel and the height per pixel according to the number of width pixels and the number of height pixels of the detection image and the width and the height of the field range; determining, by the processor, a cutting range in the detection image according to the width of each pixel and the height of each pixel; cutting, by the processor, the detection image according to the cutting range to generate a cut image; and inputting the cut image to the person detection model through the processor, so that the person detection model outputs a person detection result. Therefore, an effective image detection function can be realized.

The present application provides a speaker detection method, device, storage medium, and program product. The method includes: determining a speaker from at least one user in a video based on lip information of at least one user, and storing the speaker's voiceprint information. If the speaker's lip information is detected to be lost, the speaker in the video is determined using the stored voiceprint information. After determining the speaker based on the lip information, the present application can store the speaker's voiceprint information and, if the lip information is lost, enable voiceprint recognition to continue tracking the speaker. This reduces the possibility of speakers being unable to be correctly located during video playback due to actions such as lowering their head or leaning sideways, thereby improving the accuracy of speaker detection and enhancing the user experience.