137 results about "Object tracking algorithm" patented technology

The invention relates to the technical field of intelligent traffic monitoring, in particular to a vehicle identification and traffic incident detection method and system based on the view angle of an unmanned aerial vehicle, and the method comprises the steps: obtaining the aerial traffic video data of the unmanned aerial vehicle and the flight control data of the unmanned aerial vehicle; performing target identification on the image in the video data by adopting a target detection algorithm to obtain vehicle information; tracking the vehicle in the video data by using a target tracking algorithm, and mapping the identified vehicle to a unified coordinate system in combination with the flight control data to generate vehicle trajectory data; constructing a comprehensive road description model based on the vehicle trajectory data and the vehicle information; and forming a traffic incident recognition module based on the comprehensive road description model, and recognizing the input aerial video according to a preset traffic incident recognition rule. According to the invention, by optimizing the vehicle identification algorithm, constructing the road description model and designing the efficient traffic event identification module, the application efficiency of the unmanned aerial vehicle aerial video in the traffic monitoring field is effectively improved.

The invention discloses a home decoration construction site monitoring system and method based on artificial intelligence, and relates to the technical field of video processing. A video data set, an environment parameter set and an equipment state set in a historical construction process are collected in advance, an image semantic segmentation model is constructed based on the video data set, and construction scene division is performed; constructing a target detection model based on the video data set, generating a target spatial-temporal trajectory based on a detection result of the target detection model by using a multi-target tracking algorithm, and constructing a construction behavior recognition model based on the video data set and the target spatial-temporal trajectory; based on the recognition result of the construction behavior recognition model, the environmental parameter set and the equipment state set, constructing a risk early warning model; the safety condition of a construction site is evaluated in real time, and dynamic risk early warning is provided.

A video file may be presented via a user application that displays one or more video frames of the video file. A user request to perform an object detection for objects of a specific object type in a video frame of the video file may be received from the user application. A machine-learning model of a plurality of machine-learning models that is configured to detect objects of the specific object type may be applied to the video frame to detect an object of the specific object type in the video frame. Each of the plurality of machine-learning models may be trained to detect objects of a corresponding object type. Subsequently, an object tracking algorithm may be applied to one or more additional video frames of the video file to track the object of the specific object type across the one or more additional video frames.

The invention discloses a motion track construction method based on a visual large model, and relates to the technical field of computer vision, and the method comprises the steps: employing a target tracking algorithm based on Kalman filtering, and generating a smooth time sequence synchronization track from an original laser radar point cloud and an original video stream; dividing the trajectory into a series of candidate kinematic segments which are complete in kinematics and semantics by applying a hybrid method combining a minimum description length principle and visual language model semantic verification; performing preliminary semantic annotation on the segments by utilizing a visual language model to generate an initial annotation track; performing logic consistency refining on the initial labeling track until the initial labeling track is converged into a refined labeling track; and formatting the refined trajectory into a standard structured semantic trajectory representation character string. According to the method, a semantic gap between low-dimensional physical observation and high-dimensional driving intention is bridged, and ideal input is provided for understanding and prediction tasks of downstream complex scenes.

The invention provides a non-contact river flow measurement method and system based on a target tracking algorithm, and relates to the technical field of hydrological survey, and the method comprises the steps: when a monitoring period is triggered, recognizing and determining a plurality of target floaters in a target river region through a plurality of river cameras, and carrying out the video shooting of the plurality of target floaters, a plurality of camera videos are obtained; transmitting the plurality of camera videos to a processor, and tracking and sorting the plurality of camera videos to obtain a plurality of target floating object videos; analyzing the flow velocity of each target floater according to the plurality of target floater videos to obtain a plurality of floater flow velocities, and obtaining the current river flow of the target river region; the early-stage river flow of the last monitoring period is obtained, the river flow increment is obtained through comparison, and when the river flow increment is larger than an increment threshold value, flow abnormity early warning is conducted. The technical problem that in the prior art, a contact type measurement method is inaccurate and unreliable in river flow measurement is solved.

A video file may be presented via a user application that displays one or more video frames of the video file. A user request to perform an object detection for objects of a specific object type in a video frame of the video file may be received from the user application. A machine-learning model of a plurality of machine-learning models that is configured to detect objects of the specific object type may be applied to the video frame to detect an object of the specific object type in the video frame. Each of the plurality of machine-learning models may be trained to detect objects of a corresponding object type. Subsequently, an object tracking algorithm may be applied to one or more additional video frames of the video file to track the object of the specific object type across the one or more additional video frames.

The present invention discloses a method for identifying and tracking plasma clusters, comprising: detecting plasma clusters in a plasma cluster image based on a weight model to obtain the plasma cluster pixel coordinate position; tracking the plasma cluster pixel coordinate position based on a SORT multi-target tracking algorithm to obtain the running trajectory of the plasma cluster; it relates to the field of image processing technology, and solves the technical problem that multiple plasma clusters interfere with each other and cannot be accurately tracked; the coordinate position of plasma in the image is identified by a weight model, and it is tracked by a SORT multi-target tracking algorithm, and the difference in image grayscale gradient and the motion characteristics of the plasma cluster are fully considered, and the use of the above algorithm greatly improves the accuracy of plasma cluster identification and tracking.

The invention relates to the technical field of cableway platform intelligent management, in particular to a cableway platform tourist falling detection method, system and device based on visual identification and a medium, and the method comprises the steps: collecting a video stream of a platform region, carrying out the pedestrian detection, obtaining the position of a bounding box of a tourist, and storing the position of the bounding box; a unique track ID and continuous track data are generated for each tourist by using a multi-target tracking algorithm; for each tourist, extracting a human body posture key point coordinate in each frame of the video stream and calculating a posture feature, and when the posture feature meets a preset posture collapse judgment rule, judging that the posture collapse is suspected; carrying out time sequence fusion on the continuous tracks of each tourist, and carrying out real falling event judgment based on the judgment condition of suspected posture collapse; and matching a preset platform area according to the position of the real falling event, outputting a risk level and triggering a corresponding control instruction. According to the invention, the falling behavior can be accurately and stably identified, and the safety protection capability of the cableway platform is improved.

The invention relates to the technical field of security and protection monitoring, and discloses a parcel detection method and device and a storage medium, and the method comprises the steps: obtaining continuous frames of original images of a monitoring region, and extracting a region of interest; performing background elimination and contour extraction on the image of the region of interest to obtain a candidate target; associating multiple frames of candidate targets into a motion trail of the same target through a target tracking algorithm; and judging whether the target enters or leaves the region of interest for the first time according to the motion trail, if the target enters for the first time and the duration exceeds a set threshold, confirming that the parcel arrives, and if the target leaves, confirming that the parcel leaves. According to the method, through a dynamic trajectory analysis and regional state judgment mechanism, the problems that a traditional sensor scheme is high in false alarm rate and a video monitoring scheme is large in computing resource consumption are solved, and non-contact package state accurate detection with low computing power requirements is achieved.

The invention relates to the technical field of construction safety management. The construction personnel safety helmet wearing detection method comprises the steps that a YOLOv8 target detection model is used for carrying out safety helmet target detection on real-time monitoring video information of a construction area, and a safety helmet detection result is obtained; a semantic segmentation algorithm and a head key point detection technology are adopted to identify the safety helmet wearing state of a constructor to obtain a wearing state judgment result, a color feature extraction method is adopted to perform color classification on safety helmets to obtain a safety helmet color classification result, and a target tracking algorithm is adopted to perform target tracking on the constructor to obtain a target tracking result. The method comprises the steps of obtaining a target tracking result, performing analysis according to a preset violation detection rule based on the target tracking result, and generating safety early warning information if it is detected that a constructor does not wear a safety helmet or mistakenly wears the safety helmet. According to the invention, real-time detection of the safety helmet wearing state of the constructor is realized.

The invention discloses a radar dome camera target tracking algorithm based on complementary filtering, and particularly relates to the field of target detection and tracking, and the algorithm comprises the following steps: S1, a radar system collects the distance, speed and angle data of a target at a certain time interval, and transmits the data to a data processing unit; the dome camera monitoring equipment acquires video image data of a monitoring area in real time and transmits the video image data to the data processing unit; s2, preprocessing the distance, speed and angle data of the target, and preprocessing the video image data; according to the method, target information of the radar and the dome camera is effectively fused, the problem that the false alarm rate of the radar is high is solved, the accuracy and real-time performance of target detection and tracking and the adaptability to the complex environment are improved, human intervention is reduced, and therefore a more reliable target tracking solution is provided for the fields of security and protection monitoring and the like.

The invention relates to the technical field of image communication, in particular to an unmanned vehicle road obstacle sensing system based on video monitoring, which is characterized in that road videos and obstacle distance information are acquired, and the road videos are continuously tracked and detected through an object tracking algorithm; the method comprises the following steps: acquiring obstacle distance information of adjacent frames of video images to determine appearance matching degrees among the obstacles in the adjacent frames of video images, carrying out motion law analysis according to the obstacle distance information, determining motion feature consistency of the obstacles in the adjacent frames of video images, and when the obstacles in the adjacent frames of video images cannot be correspondingly matched in a continuous tracking detection process, judging whether the obstacles in the adjacent frames of video images cannot be correspondingly matched. According to the appearance matching degree and the motion feature consistency, the shielding degree of the obstacle is determined, the obstacle detection accuracy is improved during multi-obstacle correlation interference, the tracking accuracy between the obstacles in the adjacent frames of video images is determined according to the shielding degree of the obstacle, and the position sensing of the obstacle is performed. And the obstacle can be positioned and sensed when the obstacle is shielded.

The invention discloses a method for warning illegal operation of a storage battery based on an edge calculation box, and belongs to the technical field of urban governance algorithms, and the method comprises the steps: obtaining the video image data of a community electric vehicle shed from a video image collection device installed in the community electric vehicle shed; the video image data is processed and analyzed in real time, user behaviors can be captured and analyzed in real time by integrating a human body posture estimation algorithm, a target detection algorithm and a target tracking algorithm, and possible illegal storage battery operation behaviors can be recognized; uploading the illegal behavior analysis result to a community monitoring platform, so that the community monitoring platform displays the illegal behavior analysis result, and sharing and centralized management of information are realized; when the behavior that the user operates the storage battery illegally is recognized, the system immediately triggers sound-light alarm and pushes alarm information. According to the invention, through efficient data acquisition and analysis, real-time monitoring and accurate alarm of violation operation of the storage battery are realized.

The invention discloses a construction personnel unsafe behavior detection method based on an improved YOLO, and relates to the technical field of behavior detection, and the method comprises the steps: carrying out the improvement and training of the YOLO through the marked video data, obtaining an improved YOLO model, obtaining the video data of the operation motion of a to-be-recognized construction personnel, inputting the video data into the improved YOLO model, and combining with a multi-target tracking algorithm, and carrying out the recognition of the operation motion of the to-be-recognized construction personnel. Performing quantitative analysis on the behavior types and the behavior track characteristics of the construction personnel by applying a quantitative theory, and performing evaluation in combination with a safety evaluation model constructed based on a fuzzy analytic hierarchy process to obtain a comprehensive safety score of the construction personnel. And generating an unsafe behavior detection report according to the comprehensive safety score and the behavior safety level of the construction personnel. Through the YOLO behavior detection method, the accuracy and efficiency of unsafe behavior detection of the construction personnel are greatly improved.

The invention discloses an individual abnormal behavior detection method based on CSRT target tracking algorithm optimization, and belongs to the technical field of abnormal behavior detection, and the method comprises the steps: configuring an OpenCV library and required environment variables, initializing required parameters, and obtaining an initial tracking target; after an initial tracking target is determined, entering a main loop tracking process, and analyzing and processing any frame of video data; and outputting a real-time tracking result, and displaying the information of the tracking target and the annotation of the abnormal behavior on a video interface in real time. According to the individual abnormal behavior detection method based on CSRT target tracking algorithm optimization provided by the invention, high-precision and real-time tracking of a single target in a video is realized, similarity measurement with a space-time constrained individual behavior rule base is carried out, and rapid identification and early warning are carried out on a potential abnormal behavior mode.

The invention relates to the technical field of computer vision, in particular to a motion perception rockfall intelligent monitoring method and device based on deep learning. The method comprises the following steps: receiving a monitoring video from a monitoring area; constructing a rockfall detection and tracking framework based on a YOLOv8 target detection network and a ByteTrack target tracking algorithm; preprocessing the monitoring image, inputting the monitoring image into a YOLOv8 detection network to carry out moving target feature extraction, and outputting a rockfall detection result; and track matching is carried out by using a ByteTrack algorithm in combination with Euclidean distance and motion direction constraint, and a rockfall motion track is generated. According to the method, a traditional image algorithm and a deep learning algorithm are combined, moving target detection and multi-target tracking are constructed, and rockfall in a complex rock background is accurately captured from multiple dimensions; according to the method, the space coordinate position of the rockfall target can be accurately obtained, a complete movement track can be generated for each rockfall target, statistical information of rockfall events is effectively obtained, and a reliable basis is provided for disaster grade evaluation and early warning.

The invention discloses a target tracking method and device based on machine vision, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence or other related fields, and the method comprises the steps: obtaining a target video sequence containing at least two video frames, performing feature collection in the current video frame according to a predetermined to-be-tracked target to obtain a feature point set; performing matching analysis according to the feature point set and a previous feature point set corresponding to a previous video frame to obtain a feature point pair set; calculating a scale change value and a displacement value corresponding to the target according to the feature point pair set, and obtaining position information of a target frame in the previous video frame; and updating the position of the target frame in the current video frame according to the scale change value, the displacement value and the position information of the target frame in the previous video frame to obtain a target tracking result. Through the target tracking method and device, the technical problem that the tracking precision and continuity of a target tracking algorithm in a complex changing environment are reduced in the related technology is solved.

The present invention discloses a method for measuring the tracking performance of a pan-tilt based on machine vision technology. The camera is placed facing the display screen directly. The display screen plays a pre-set video of the target movement trajectory. The camera captures images and tracks the target in the display screen in real time, and acquires all the image sequences during the tracking process. Then, the image sequences are analyzed and processed to obtain the measurement result of the pan-tilt tracking performance. The present invention can be realized under simple devices in daily life, without other devices such as turntables. It combines the target tracking algorithm with the pan-tilt well. On the already built structure, if it is necessary to unidirectionally measure the tracking performance of the algorithm or the pan-tilt, it can also be realized on this structure by controlling variables.

The application discloses a kind of based on target tracking algorithm's gas detection operation compliance identification method, belong to gas detection technical field.The method includes: high-definition video camera is arranged in the methane detection area of mine, real-time acquisition video data in methane detection area and image is preprocessed;By YOLO target detection method, the gas detector in the methane detection area in video is identified;If detecting that gas detector hand holds gas detector and arrives specified methane detection area on time, then it is judged as compliance operation, otherwise it is judged as non-compliance operation;After verifying that gas detector hand holds gas detector arrives specified methane detection area on time, the gas detection operation of scene is tracked in real time by CSRT target tracking algorithm;Then with standardized operation is compared, if gas detection operation meets standardized operation, then it is judged as compliance operation, otherwise it is judged as non-compliance operation.The application can improve the reliability and safety of gas detection operation, reduce the safety risk brought by illegal operation or non-operation.

Techniques for performing object tracking of objects in video images using both an object detection algorithm and an object tracking algorithm. Because the object detection algorithm is generally slower but more accurate than the object tracking algorithm, only certain sampled video frames are subjected to the object detection algorithm. Non-sampled frames use the object tracking algorithm to locate objects, using the most recent previous object detection frame as a starting frame. In this way, the object tracking algorithm is periodically, effectively calibrated using the object detection algorithm, by hiding the latency introduced by the object detection algorithm using the lagging detection results.

The present invention discloses a method, device and storage medium for identifying abnormal add-on purchases and intelligently returning purchases, and belongs to the technical field of intelligent shopping carts. The method comprises: inputting a collected shopping process video frame by frame into a pre-trained ten-category target detection network, and outputting detection frame coordinates and category information of all commodities in each frame image; inputting the detection frame coordinates and category information into a target tracking algorithm, and generating trajectory information of each shopping commodity; performing a trajectory image filtering operation on the trajectory information, and outputting a filtered single-frame trajectory image; inputting the single-frame trajectory image into a pre-trained multi-attribute classification network, and outputting classification features of the corresponding commodity; comparing the classification features of the commodity with classification features in a pre-built multi-attribute standard library, and determining that abnormal add-on purchase behavior exists if any feature is inconsistent; the present invention realizes the process of identifying abnormal add-on purchases and intelligently returning purchases, effectively improving the settlement rate, reducing supermarket losses, and enhancing the user shopping experience.

The present invention relates to a long-term video object tracking method based on a Siamese network for joint tracking and detection, and proposes a target tracking strategy for joint tracking and detection. When the target to be tracked in a video sequence is completely occluded or leaves the field of view area, that is, when the target disappears, traditional target tracking algorithms based on the Siamese network often cannot locate the reappearing target. After the target disappearance discrimination module of the present invention determines that the target has disappeared, it chooses to use the target detector to perform a full-image detection. When the target reappears, the detector gives all objects of the same class, and the target to be tracked is found by comparing the similarity with the template, and the tracker is restarted; using the above method can avoid the problem of tracking failure caused by target disappearance and improve the tracking accuracy and success rate.

A target tracking method for a hyperspectral video, which initializes weight parameters in a hyperspectral target tracking algorithm; reads an initial frame of a tracking video as an initial template and an online update template image for target tracking, and simultaneously reads the position information of the target in the initial frame; reads the next frame of the tracking video and performs cropping processing to use it as a search image for target tracking; uses the initial template, the online update template image and the search image as inputs of a hyperspectral target tracking algorithm, reduces the dimensions to three-channel images through a dimensionality reduction module, and then inputs the image into a feature extraction network after low-rank adaptive learning, inputs the obtained feature information into a target position decoder, outputs the predicted position of the target in the current frame and its confidence, and determines whether to update the online update template image according to the confidence; repeats each frame of the tracking video and records the predicted position of the target in each frame of the video; the invention has the advantages of high target tracking accuracy, strong extracted feature representation, low training cost, and the like.

The invention belongs to the field of target tracking based on a deformable model, and particularly relates to a target tracking area self-adaptive extraction and updating method. Comprising the following steps of: measuring a saliency region of a target template by adopting a spectral residual method based on a human visual attention mechanism, and measuring texture richness of the target template by utilizing dispersion in an edge direction; carrying out feature fusion on a measured result to obtain a joint suitability confidence coefficient; adaptively determining the number and the size of tracking areas according to the area and the aspect ratio of the target, and extracting a target tracking area according to the joint suitability confidence coefficient; and adaptively judging whether the tracking area needs to be updated or not by adopting a tracking area motion consistency evaluation function based on a local trajectory. According to the method, a multi-feature fusion tracking area automatic extraction and updating method is adopted in a target tracking algorithm, so that an unreliable tracking area is updated in time when the target is deformed or partially shielded in the tracking process, and the adaptability of a tracker to the appearance of the target is kept.

The application discloses a tracking target loss detection method based on color histogram distance, comprising: obtaining a tracking image, wherein the tracking image comprises a target region and a background region; determining a tracking frame scaling ratio based on the target region of the tracking image to adjust tracking frame parameters to obtain a target center region, so that the ratio of the number of pixels of a target object in the target center region to the number of pixels of the background satisfies a preset condition; generating a color histogram according to the target center region; storing color histogram feature values in time sequence based on the color histogram to detect target loss according to the feature values; wherein the tracking frame is a rectangle, and the tracking frame parameters comprise a side length and a rotation angle. The application can accurately detect the occurrence of abnormal feature values to improve the long-time working reliability and intelligent level of a target tracking algorithm and an application system.

The invention provides a flapping-wing aircraft target detection method and system based on a long-focus camera and a short-focus camera. The method comprises the following steps: acquiring a long-focus video stream and a short-focus video stream through a two-axis binocular holder; acquiring a detection target in the long-focus and short-focus image by adopting a target detection algorithm; performing correlation matching on the detection targets in the long and short focus images by adopting a matching algorithm; target information fusion is carried out on the targets which are successfully associated and matched; performing target data fusion on the fused target and the target which is not successfully associated and matched, and integrating the target data into a short-focus image; performing trajectory prediction and tracking on the target by adopting a target tracking algorithm; and selecting a tracking target to control a pan-tilt motor to realize target tracking. The advantages of large visual field of the short-focus camera and good small target detection effect of the long-focus camera are considered, the detection distance and the detection tracking performance of the ornithopter can be effectively improved, and the actual use requirements of the ornithopter are fully met.

The application discloses a production line specification operation behavior intelligent detection management system. The technical scheme points of the system are as follows: the system adopts a hierarchical fusion type architecture, and comprises a data acquisition layer, a pretreatment layer, a core AI algorithm layer, a business logic layer and a data storage and output layer from top to bottom. The data acquisition layer collects production line high-definition video streams and account data; the pretreatment layer optimizes image quality and decodes; the core AI algorithm layer integrates object recognition, behavior recognition, pose estimation and multi-target tracking algorithms, and perceives employee operation behavior in all dimensions; the business logic layer realizes five-dimension compliance determination, real-time line following snapshot storage and two-hour storage data cycle; and the data storage and output layer archives data and provides monitoring, report and traceability functions. The application solves the problems of low manual evaluation efficiency, poor working condition adaptation and single supervision dimension in the prior art, realizes real-time traceable intelligent evaluation of industrial production line operation, and is suitable for fine management scenes of various industrial production lines.

The application discloses an unmanned ship-oriented target detection and tracking method and system, and the method comprises the following steps: collecting a video frame of a water surface environment; establishing a YOLOv8 target detection model and a ByteTrack target tracking model, wherein the YOLOv8 target detection model adopts an HGNetv2 backbone network structure, and the ByteTrack target tracking model comprises a BYTE association strategy; inputting the video frame of the water surface environment into the YOLOv8 target detection model for water surface target detection, and outputting a water surface target detection frame set; inputting the water surface target detection frame set and the video frame of the water surface environment into the ByteTrack target tracking model for water surface target tracking, and outputting a water surface target tracking path set. The application solves the problems in the existing unmanned ship target detection and tracking method, i.e., the relatively large calculation amount and parameter amount in water surface target detection by using a Mobilenetv3-YOLOv4 target detection algorithm, and the possible missing detection situation in water surface target tracking by using a KCF correlation filtering target tracking algorithm, thereby reducing the tracking accuracy.

The present invention discloses an auxiliary collision warning method based on a monocular camera. The method uses the ECO tracking algorithm in the foreground to output an estimated tracking result of the vehicle, while the backend uses the SSD target detection algorithm, which is slower but has higher target detection accuracy, to detect the vehicle, obtaining a more accurate target detection frame. The vehicle tracking result outputted from the foreground is corrected by the target detection frame, thereby improving the timeliness of the overall method. The ECO tracking algorithm is then used as the main method to quickly give a prediction frame for target detection of the object in the next frame of the image, thereby alleviating the high latency problem of the target detection neural network. The present invention conducts detection and tracking in parallel, can quickly make collision warnings, and improve the operating speed of the system; compared with other target tracking algorithms, it can obtain the position of the target more accurately, thereby greatly reducing the occurrence of errors and improving the reliability of the system.

The invention provides a method for dynamically tracking personnel in a grain depot. The method comprises the following steps: training a target detection algorithm in advance; intercepting an image sequence in a video stream of a camera at the front end of the granary at a certain time interval; inputting the image sequence into a target detection algorithm to obtain a personnel position coordinate set, a bin gate region set and a grain surface region set; inputting the personnel position coordinate set into an improved Deep-sort multi-target tracking algorithm, tracking personnel trajectories and identifying personnel IDs (Identity); obtaining a personnel gravity center position set through the personnel position coordinate set, and judging whether the personnel enter and exit from the warehouse or not according to the position relation between the personnel gravity center position set and the warehouse door area set and the grain surface area set; according to the invention, dynamic statistics is carried out on the number of people in the grain depot based on the improved Deep-sort multi-target tracking algorithm, and the number of people and dynamic changes can be accurately identified.