102 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 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 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 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 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 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 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.

The invention discloses an object tracking method and device, a storage medium and electronic equipment, and relates to the technical field of computer vision and artificial intelligence. The method comprises the following steps: acquiring historical state data of a scene object; determining an inter-frame size difference and a size standard difference based on the historical state data; determining an anomaly detection result of the scene object according to the inter-frame size difference and the size standard deviation; when the abnormal detection result shows that the size of the detection frame is abnormal, adjusting observation noise in the initial tracking algorithm to obtain a target tracking algorithm; and tracking the scene object by using a target tracking algorithm to obtain a tracking trajectory. The object tracking method and device solve the technical problems of low accuracy and poor stability of an object tracking method provided in the related technology.

The present application discloses a work clothes detection method, system, equipment and medium combining yolov8 and target tracking, which relates to the field of safety detection technology. The method comprises acquiring surveillance video data of a target monitoring area and performing data preprocessing; performing work clothes target detection on the preprocessed surveillance video data in a preset work clothes detection model to obtain a work clothes detection result; performing target tracking analysis based on the work clothes detection result to obtain a work clothes tracking result of a continuous video stream; constructing a preset work clothes detection model in combination with the yolov8 algorithm and the target tracking algorithm; generating a defect detection mark based on the work clothes detection result and triggering an alarm instruction; acquiring geographic location information based on the work clothes tracking result, performing spatial matching analysis based on the geographic location information and the work clothes tracking result to obtain a safety target detection result of the corresponding target monitoring area; the present application provides a deep analysis method combining computer vision and deep learning technology to improve the efficiency of safety monitoring.

The invention relates to a parameter-adjustable target tracking algorithm verification method and verification system. The verification method comprises the following steps: inputting an image sequence in a real use scene; inputting image parameters; inputting tracking parameters; setting an image start frame number; and starting target tracking calculation and image stretching display. The parameter-adjustable target tracking algorithm verification method and verification system can be used in a laboratory or a simulation environment. The system supports a plurality of image formats (such as. Raw,. Dat,. Jpg,. Png), and has a flexible algorithm parameter dynamic adjustment function. According to the method, the target tracking algorithm can be quickly verified and iterated, the real scene image sequence can be repeatedly used for multiple times, the verification efficiency is remarkably improved, and the dependence on the actual external field environment is reduced.

The invention discloses a target behavior recognition method and device for a boundary area. The method comprises the following steps: collecting video data of the boundary area; performing multi-target identification and tracking on the video data by adopting an improved target tracking algorithm, and giving a current track of each target; based on the current track of each target, determining a three-dimensional motion track of each target within a predetermined period after the current moment; and according to the current track and the three-dimensional motion track of each target, a target behavior recognition result is given in combination with the structural characteristics of the boundary region. The target behavior recognition precision of the boundary region can be improved.

The application discloses a safety helmet wearing detection method and system, which comprises a data acquisition module, a target detection module, a target tracking module and a camera equipment automatic control module, adds on-site monitoring images on the basis of the disclosed safety helmet wearing detection data set, obtains a new data set after data enhancement, constructs a safety helmet wearing detection network, simultaneously optimizes the initial anchor frame and the hyperparameter by using a genetic algorithm, trains the network to obtain a safety helmet wearing detection model, then combines a target tracking algorithm with a monitoring device to realize automatic tracking of the camera equipment on the target working personnel. The tracking algorithm automatically analyzes and extracts the motion trajectory features of the target, considers the interframe correlation, thereby making up for the shortcomings of the visual target detection method, improving the detection accuracy and the identification degree of small targets. The application can effectively solve the problems of small target missed detection in the construction environment and false detection in the complex background, and realizes automatic tracking detection of the camera equipment on the illegal target.

The invention discloses a target tracking algorithm-based sowing seed hole-falling counting method of a sowing machine. The method comprises the following steps of A, acquiring a sowing seed hole-falling video in a sowing process of the sowing machine in real time; b, constructing a sowing grain hole-falling data set based on the hole-falling video; c, constructing a sowing seed detection model for detecting and positioning sowing seeds; d, training to obtain a trained sowing grain detection model; and E, performing real-time target detection on the seeding grain hole-falling video to be subjected to hole-falling counting from a first frame by using the trained seeding grain detection model, and calculating the inter-frame grain distance of the same grain among different frame numbers by combining a target tracking algorithm from a second frame, when the inter-frame grain distance meets a set threshold condition, continuous tracking is carried out, and hole falling event judgment and counting are completed. According to the invention, the sowing grain hole falling condition in the sowing process can be accurately detected in real time, the non-sowing position and the sowing quantity are fed back, and the sowing precision and efficiency are improved.

This invention discloses an intelligent directing system for dance scenes, comprising: a streaming decoding module for acquiring and decoding video streams from multiple cameras; a blur detection module for evaluating the clarity of each frame in the video stream; a dance formation change judgment module for analyzing the decoded video frames based on a target tracking algorithm to detect and determine whether the dancers' formations have changed; a lead dancer recognition module for identifying the lead dancer in the decoded video frames using a deep learning-based human pose recognition algorithm; a shot switching module for obtaining a shot transition probability matrix and a shot playback duration matrix based on statistical results of dance program video data, used to guide the shot dwell time and switching channel selection based on shot combination when there are no formation changes and the lead dancer has no special movements; and an intelligent directing logic switching module for automatically executing video switching instructions according to the needs of the dance scene, achieving intelligent directing effects. This invention enables intelligent directing of dance scenes, improving the efficiency and quality of program production.

The application relates to the technical field of video object tracking, and proposes a video object tracking trajectory generation method, an electronic device and a program product. The method comprises the following steps: after completing a video object tracking task, obtaining frame-by-frame detection results of the video object tracking task, wherein the frame-by-frame detection results comprise object detection boxes contained in each video frame of a video file respectively, and object names corresponding to each object detection box respectively; converting the frame-by-frame detection results into trajectory results aggregated according to the object names and sorted according to frame numbers, wherein the trajectory results comprise arrangement trajectories of all object detection boxes corresponding to each object name in turn appearing in each video frame of the video file. By using the method, the frame-by-frame detection results output by a video object tracking algorithm can be automatically converted into object trajectory results required by a business layer, so that the business layer can carry out subsequent object retrieval, display and evidence association and the like.

The application provides a method for tracking and counting oil pipe materials, which comprises the following steps: S1, preprocessing and data enhancement processing are performed on pre-acquired images of different types of oil pipe materials, and an oil pipe material target detection model for detecting oil pipe materials is constructed and trained; S2, the oil pipe materials in an oil pipe material video are identified through the oil pipe material target detection model, and each oil pipe material is given a unique ID tag according to the identification result, and the oil pipe materials are counted and tracked according to the ID tags by using a multi-frame cooperative unified calculation algorithm. The application also provides an electronic device. The application gives each oil pipe material a unique ID tag by using a target tracking algorithm, realizes accurate detection and tracking of oil pipe materials, and effectively solves the situation that a single frame of picture cannot contain all oil pipe materials laid on the ground by using a multi-frame cooperative unified calculation algorithm, realizes accurate counting of oil pipe materials, and is more in line with actual application requirements.

The invention discloses a method and a device for identifying idling behaviors of security officers and electronic equipment, and relates to the field of artificial intelligence and big data, and the method comprises the steps: obtaining a to-be-identified image frame set; performing target identification on the image frame set, and performing tracking identification on a security officer in the image frame based on a target tracking algorithm under the condition that the security officer is on duty in the image frame to obtain a second identification result; performing target detection on the image frame set, and performing association identification and key point identification on the security officer and the handheld terminal under the condition that a target detection result indicates that the handheld terminal exists in the image frame to obtain a third identification result; and generating an idling behavior recognition result based on the first recognition result, the second recognition result and the third recognition result. According to the method and the device, the technical problem that the identification result is low in accuracy due to the fact that the mode of identifying the idling behavior of the security officer based on target detection depends on a single-frame image in the prior art is solved.

The present application provides a method, apparatus, electronic device, and readable storage medium for occluding frame correction. The method includes: obtaining first occlusion data and second occlusion data of a target video, where the first occlusion data represents the position of an occlusion frame at time T, the second occlusion data represents the position of the occlusion frame at time T+1, and T is a positive integer; processing the first occlusion data and the second occlusion data according to a Kalman filter to obtain correction information; and correcting the position of the occlusion frame at time T+1 according to the correction information. The present application inputs the position of the occlusion frame of each video frame in the target video and the position of the occlusion frame of the corresponding previous video frame into a Kalman filter to obtain correction information for correcting the position of the occlusion frame of each video frame output by the target tracking algorithm, reducing the position offset between different occlusion frames corresponding to adjacent video frames, ensuring the picture coherence during the playback of the target video, and improving the playback effect of the target video.