575 results about "Person detection" patented technology

Systems and methods are provided for machine learning based monitoring. Image data from a camera is received. On the hardware accelerator, a person detection model based on the image data is invoked. The person detection model outputs first classification result. Based on the first classification result, a person is detected. Second image data is received from the camera. In response to detecting the person, a fall detection model is invoked on the hardware accelerator based on the second image data. The fall detection model outputs a second classification result. A potential fall based on the second classification result is detected. An alert is provided in response to detecting the potential fall.

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 discloses a roadway cleaning robot autonomous navigation and operation system based on multi-sensor fusion. The roadway cleaning robot autonomous navigation and operation system comprises a mechanical system, a control system and a power system. The mechanical system comprises a crawler chassis, a mechanical arm and a big arm turntable; the control system software part presentation layer comprises a user interaction and management module, the business layer comprises a task planning and decision module, and the function layer comprises a robot autonomous walking control module, a roadway deformation detection module, a mechanical arm accessory replacement control module and a mechanical arm roadway cleaning control module. The sensing layer comprises an infrared person detection module, a laser radar mapping positioning module, a mechanical arm visual positioning module and a mechanical arm state feedback calculation module; the hardware part comprises a laser radar, an IMU, a sensor and the like. Various sensor data are fused, accurate positioning, autonomous walking, mechanical arm visual positioning, roadway deformation detection, accessory automatic replacement, roadway automatic cleaning and the like of the robot are achieved, and the intelligent level and the working efficiency of the robot 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.

To appropriately perform screen luminance control and system operation control by using face detection.SOLUTION: An information processing device performs person detection processing for detecting the presence of a person and face authentication processing by detecting a face region where a face is photographed from a photographed image, instructs transition of a system operation state and change of screen luminance on the basis of a detection result of the person detection processing, and executes unlock processing for unlocking the system on the basis of the authentication result of the face authentication processing. Further, when the state transitions from a state in which the presence of a person has been detected to a state in which the presence of a person is no longer detected by the person detection processing, the information processing device lowers the screen luminance, starts unlock processing by using face authentication processing by locking the system before a first time period has elapsed, and after the first time period has elapsed, transits the system to a waiting state. Further in an image luminance lowered state, the information processing device continues the image luminance lowered state regardless of the person detection processing in response to locking of the system.SELECTED DRAWING: Figure 4

This application provides a pedestrian detection method, a method for training a pedestrian detection model, an electronic device, and a computer-readable storage medium. The pedestrian detection method includes: acquiring an image to be recognized; inputting the image into a multi-task recognition network to acquire a predicted pedestrian location and a predicted pedestrian attribute in parallel; and correlating the predicted pedestrian location and the predicted pedestrian attribute to output a detection result. The pedestrian detection method utilizes the multi-task recognition network to acquire the predicted pedestrian location and the predicted pedestrian attribute in parallel, and the detection result containing the predicted pedestrian location and the predicted pedestrian attribute is acquired only by inputting the image to be recognized once, thereby improving detection efficiency, quickly obtaining the detection result and saving device resources.

In various examples, sensor data (e.g., image and / or RADAR data) may be used to detect occupants and classify them (e.g., as children or adults) using one or more predictions that represent estimated age (e.g., based on detected limb length, a detected face) and / or detected child presence (e.g., based on detecting an occupied child seat). In some embodiments, multiple predictions generated using multiple machine learning models (and optionally one or more corresponding confidence values) may be combined using a state machine and / or one or more machine learning models to generate a combined assessment of occupant presence and / or age for each occupant and / or supported occupant slot. As such, the techniques described herein may be utilized to detect child presence, detect unattended child presence, determine age or size of a particular occupant, and / or take some responsive action (e.g., trigger an alarm, control temperature, unlock door(s), permit or disable airbag deployment, etc.).

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 provides a data processing method based on indoor environment monitoring and a related device. The data processing method is applied to a server in an environment monitoring system of a target indoor space, and the environment monitoring system further comprises an indoor monitoring device, an environment detection sensor and an environment adjusting device. The method comprises the following steps: acquiring monitoring video data according to a monitoring period; executing a person detection operation according to the monitoring video data to determine a detection result; if the detection result shows that the target user exists in the target indoor space, determining a target parameter value range matched with the target user; and according to the target parameter value range and the real-time parameter values acquired by the environment detection sensor, controlling the environment adjusting device to adjust the environment parameters. Thus, the server interacts with various devices, and adaptive adjustment can be performed on the indoor environment according to the difference of users in the indoor space and the difference of actual environment parameters, so that the requirements of the users are met, and the comprehensiveness and the flexibility of the environment monitoring system are improved.

The invention belongs to the field of machine learning and target detection, and discloses an infrared and visible light fusion pedestrian detection method based on weight autonomous optimization. According to the method, feature extraction is carried out on infrared and visible light images through a convolutional neural network; and through a learnable proportionality coefficient, the extracted features are superposed back to the original image according to respective proportions. In the feature fusion stage, the fusion proportion of the infrared and visible light modes is dynamically adjusted through a learnable fusion weight coefficient. In the training stage, end-to-end optimization is carried out by using a large-scale annotation data set, so that the superposition proportion and the fusion weight alpha of each mode can be adaptively adjusted to adapt to different illumination conditions and scene changes, and the generalization ability of the model is remarkably improved. In a robot vision system for night pedestrian detection, an infrared and visible light modal fusion method based on machine learning weight optimization is adopted, and the accuracy and robustness of pedestrian detection are effectively improved.

An elevator system includes a hoistway; an elevator car configured to travel in the hoistway; a pit located at a bottom of the hoistway; a safety chain configured to enable or disable motion of the elevator car; and a sensor assembly configured to initiate opening the safety chain to disable motion of the elevator car upon detection of a person in the pit.

A smart doorbell, for a security system, which is capable of performing functions including, person detection, facial recognition, motion detection, detection of baby or pet running away, weapon detection, fire detection, detection of parcel left at porch, parcel theft, dog poop detection, vehicle detection, animal detection, eavesdropper detection, a person jumping from backyard wall, and intruder detection. The functions are detected through a camera of the doorbell and, subsequently, AI-based alerts are generated.

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 embodiment of the invention discloses a people counting method and device based on an image full scene, equipment and a medium. The method comprises the following steps: detecting a to-be-detected image to obtain an initial head and shoulder frame and a whole person frame in a whole scene of the image; a head and shoulder frame corresponding to the whole person frame is obtained according to the whole person frame and serves as a whole person head and shoulder frame; according to a matching result of the initial head-shoulder frame and the whole-person head-shoulder frame, screening a target head-shoulder frame, and according to the screened initial head-shoulder frame and the whole-person head-shoulder frame, determining a people counting result; wherein the target head and shoulder frame comprises an initial head and shoulder frame or a whole head and shoulder frame. According to the technical scheme, the whole person frame is converted into the whole person head and shoulder frame, the size difference between the whole person detection frame and the head and shoulder detection frame is reduced, and the accuracy of people counting can be improved based on multi-label matching.

To suppress unnecessary activation by a person detection process when a transition to a standby state is made by a trigger other than the detection of a person's departure. [Solution] The information processing device has a detection process having a first detection mode that transitions to the second detection mode when it is determined that a person is no longer present, a second detection mode that returns to the first detection mode if movement of an object is detected within a certain period of time and transitions to the third detection mode if movement of an object is not detected within the certain period of time, and a third detection mode that detects objects with low power consumption, and when control is performed to the first operating state or a second operating state with lower power consumption than the first operating state based on a trigger of a process other than the detection process, if it is determined that a person is present in the first detection mode, the detection time in the second detection mode is changed to 0, and if it is determined that a person is no longer present in the first detection mode, the detection process transitions to the third detection mode without waiting for the certain period of time.

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 belongs to the technical field of intelligent traffic and computer vision crossing, and particularly relates to a pedestrian detection method based on multispectral fusion. Aiming at the bottleneck problem that single-source data sensing reliability is insufficient in a complex traffic scene, the invention provides a visible light-infrared double-flow collaborative pedestrian detection framework based on a neural network. According to the method, complementary semantic information between visible light and infrared images is fully mined, a multispectral feature dynamic interaction mechanism composed of three stages of feature decoupling, interaction and reconstruction is established, and robust multispectral representation is generated through a feature fusion module based on channel attention. Compared with an existing method, the method has the advantages that target feature discrimination can be remarkably improved, higher adaptability is achieved in multi-scene, multi-scale and multi-target detection tasks, accurate positioning and recognition of pedestrian targets in the all-weather environment can be achieved, and reliable technical guarantee is provided for an intelligent traffic monitoring system.

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.

A smart doorbell, for a security system, which is capable of performing functions including, person detection, facial recognition, motion detection, detection of baby or pet running away, weapon detection, fire detection, detection of parcel left at porch, parcel theft, dog poop detection, vehicle detection, animal detection, eavesdropper detection, a person jumping from backyard wall, and intruder detection. The functions are detected through a camera of the doorbell and, subsequently, AI-based alerts are generated.

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 lightweight pedestrian detection method based on BASFPN multi-scale feature fusion, and belongs to the technical field of artificial intelligence and computer vision target detection. The technical problems of low precision and insufficient robustness in the aspect of detection of small target pedestrians and shielded pedestrians are solved. Comprising the following steps: S1, acquiring a pedestrian detection video, and preprocessing the video into an image; s2, performing data mixing enhancement processing on the acquired data; s3, constructing a lightweight network model MobileViT as a feature extraction network of the model; s4, constructing an FCOS network model; s5, a BASFPN feature pyramid layer is designed; and S7, inputting the data set into the improved model for training iteration. According to the invention, by improving the FPN structure and the fusion strategy, the utilization and expression ability of the model to different scale features is enhanced, so that the overall performance of pedestrian detection is improved.