192 results about "Object tracking algorithm" patented technology

The invention discloses an autoregressive visual target tracking algorithm based on token fusion, and the algorithm specifically comprises the following steps: 1, obtaining a public visual target tracking data set, and converting the data set into a central coordinate format; 2, preprocessing an initial template of a given video sequence, an online template at the current moment and a search image to obtain visual embedding of the initial template, a dynamic template and the search image; the method comprises the following steps: preprocessing an angular coordinate sequence of a target frame in previous historical N frames of a current search image of an input video to obtain historical track word embedding of the current search image; 3, constructing an autoregressive visual target tracking model based on token fusion, and designing a loss function; step 4, obtaining a final autoregressive visual target tracking model based on token fusion; the method solves the problem of insufficient model feature extraction capability caused by insufficient utilization of spatio-temporal information and redundant background information of a search area in an existing method.

The invention relates to the technical field of bird repelling, in particular to an intelligent bird recognizing, tracking and repelling device, which comprises a video monitoring module, a tracking module, a bird repelling module, a control module and a power supply module, and is characterized in that the video monitoring module is used for monitoring bird conditions in a no-fly zone in real time by using a camera; the laser bird repelling module is used for repelling birds by using a laser; the control holder module is used for installing the video monitoring module and the laser bird repelling module; and the core control module is used for collecting the video information transmitted by the camera in the video monitoring module through identification and analysis, and detecting and tracking the birds in the no-fly zone. According to the invention, multi-algorithm real-time processing of the STC target tracking algorithm, the ECO algorithm and the YOLO-V3 detection algorithm is realized, the operation efficiency of the system is improved, the YOLO-V3 detection algorithm can adjust the size of a tracking frame according to the size of an identified target, targets of different sizes can be better adapted, the tracking accuracy is improved, laser is used for repelling birds, and the tracking accuracy is improved. The bird repelling effect is improved, and meanwhile safety is considered.

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 video monitoring method and device. The method comprises the following steps: at each acquisition moment, acquiring a video frame image of a pan-tilt camera in a current visual angle range; analyzing the video frame image by using a multi-target tracking algorithm to obtain a first position detection frame of each to-be-tracked object in the video frame image at the current acquisition moment and a first position prediction frame of each to-be-tracked object at the next acquisition moment; determining a target visual angle range comprising each first position detection frame and each first position prediction frame; and adjusting the current visual angle range of the PTZ camera to the target visual angle range by adjusting the rotation angle and / or the focal length of the PTZ camera. According to the invention, the technical problems of lack of flexibility in video acquisition by a camera and poor target tracking effect in a related video monitoring scheme are solved.

The embodiment of the invention provides a personnel behavior detection method based on multi-modal data and related equipment, and is applied to the technical field of image recognition processing. A to-be-processed video is acquired, and a to-be-processed image frame is extracted from the to-be-processed video. And extracting multi-modal data from the to-be-processed image frame by using a target detection algorithm, a target tracking algorithm and a human body posture estimation algorithm, wherein the multi-modal data at least comprises a personnel detection frame, a personnel image and a personnel posture feature map. And inputting the personnel detection frame, the personnel image and the personnel posture feature map into a pre-constructed abnormal behavior detection model to obtain a behavior detection result output by the abnormal behavior detection model. Image data is obtained from the video data, the personnel action has continuity, and the image data is close to an actual website scene; a multi-modal data fusion abnormal behavior detection model is used to make up for the data limitation and fusion suitability among the personnel image, the detection frame and the attitude feature, so as to improve the behavior abnormality recognition accuracy.

The invention discloses a sheep rumination behavior recognition method and system based on deep learning, and belongs to the technical field of livestock and poultry healthy breeding, and the method comprises the steps: obtaining video data for sheep rumination behavior recognition, constructing a training set, and training a YOLOv8 target detection model; video data to be recognized are converted into video frames, the video frames are input into the trained YOLOv8 target detection model, a detection frame and features of a sheep head image with an ID mark frame in the video frames are obtained, the position and state of the next frame of a target are predicted through a Deepsort target tracking algorithm, optimal matching is carried out on the multiple targets of the front frame and the rear frame, and the target detection accuracy is improved. Obtaining a plurality of target updated detection frames and ID numbers; by judging that the interval between the current frame and the previous frame exceeds the preset frame number, when the interval exceeds the preset frame number, the current frame is reserved, all previous frames are removed, a sheep rumination classification video data set is constructed, and rumination behaviors are identified through a constructed neural network model. The method can accurately identify the ruminant behavior of the sheep.

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 discloses an embedded aerial photography multi-target tracking system, and belongs to the technical field of image processing. The aerial photography multi-target tracking system comprises a remote end system and a ground end system. The remote end system is composed of an image acquisition and preprocessing module, a target detection module and a target tracking module. The image acquisition and preprocessing module acquires aerial image data by calling a camera and performs preprocessing operation on the data to enable the data to accord with the input format of the target detection module. And then, outputting coordinates of a detection target in the picture through a lightweight target detection module. And inputting the detected target coordinates into a target tracking module, tracking the target through the target tracking module, and distributing a unique ID number. The remote end pushes a video stream to the ground end through an RTSP protocol, the ground end analyzes and displays the video stream, and the ground end can also control parameters of the image acquisition and preprocessing module, the target detection module and the target tracking module of the remote end and transmit the parameters to the remote end through a TCP protocol. The method can be deployed in an unmanned aerial vehicle airborne system, and compared with a traditional target tracking algorithm, the method has lower performance consumption and higher detection performance in a target tracking task.

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.

The invention relates to the technical field of image processing and machine vision, in particular to an anti-occlusion target tracking method integrating Kalman filtering and particle filtering, which is low in cost, low in complexity, high in real-time performance and high in stability, and compared with the prior art, the robustness and the accuracy of a target tracking algorithm in an occlusion scene can be remarkably improved, and the target tracking method has the advantages of high robustness and high robustness. Target tracking loss caused by shielding is effectively avoided; according to the invention, the anti-shielding function is realized only depending on the algorithm, the hardware cost of the system is reduced, and the target tracking algorithm is simpler, more convenient and lighter in engineering application, so that the anti-shielding target tracking method provided by the invention has the characteristics of low cost, low complexity, strong real-time performance and high stability; the target can be continuously tracked in a complex scene, and the method has important application value and wide application prospect.

The invention discloses a target tracking algorithm based on feature complementary fusion and key frame template updating, and relates to the technical field of image processing. Comprising the following steps: constructing a target tracking model using a template updating strategy based on a key frame, inputting a template frame and a search frame into the target tracking model, and extracting template features and search features; independently carrying out convolution on each channel of the template features and the search features to obtain local features; global information corresponding to the template features and the search features is obtained; obtaining the similarity between the global information corresponding to the template features and the global information corresponding to the search features, and fusing the template features, the search features and the similarity; and determining whether the position of each pixel in the fusion features is a foreground or a background, and predicting the bounding box position of the tracking target in the fusion features. The method not only considers local features, but also considers the influence of a front-layer attention mechanism result on subsequent attention operation, thereby improving the accuracy of target tracking.

The invention discloses an intelligent broadcasting director system for a dance scene, and the system comprises a pull stream decoding module which is used for obtaining video streams from a plurality of cameras and decoding the video streams; the fuzzy detection module is used for evaluating whether each frame of image in the video stream is clear or not; the dance formation change judgment module is used for analyzing the decoded video frame based on a target tracking algorithm and detecting and judging whether the formation of the dance actor changes or not; the leader dance recognition module is used for recognizing leader dance actors in the decoded video frames through a deep learning human body posture recognition algorithm; the scene switching module is used for obtaining a scene-based shot transition probability matrix and a shot playing duration matrix according to a statistical result of the dance program video data, and is used for guiding the staying duration of a shot and switching channel selection according to scene matching when no formation change exists and the leader dance does not have special actions; and the intelligent director logic switching module is used for automatically executing a video switching instruction according to the demand of the dance scene so as to realize an intelligent director effect. According to the invention, intelligent broadcasting of a dance scene can be realized, and the efficiency and quality of program production are improved.

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 a data processing method for updating a target image based on contextual information feature fusion, belongs to the technical field of computer vision, and solves the problem that a tracking target is lost when the appearance of the tracking target is greatly changed in the prior art. Comprising the following steps: acquiring a video image sequence including a to-be-tracked target and a to-be-tracked target image; inputting the to-be-tracked target image and the video image sequence into a trained target tracking model to obtain a target tracking result; wherein the target tracking model is used for tracking each frame of image in a video image sequence by using target template features to obtain a to-be-tracked target; the target template feature is dynamically updated according to the confidence score of the target area identified by each frame of image in the video image sequence. The target template is dynamically updated by fusing the context information, and the performance and robustness of a target tracking algorithm in a complex environment are improved.

The invention relates to the technical field of image recognition, and particularly provides a road target monitoring method, system and device and a storage medium, and the method comprises the steps: obtaining a road monitoring video stream, converting the road monitoring video stream into an image frame sequence, and carrying out the preprocessing of image frames; identifying a road target in each image frame by using a target detection algorithm, and generating a track of the road target in the image frame sequence by using a multi-target tracking algorithm; performing anomaly analysis on the trajectory based on a preset rule, and identifying an abnormal trajectory; and obtaining a confidence score of an abnormal road target to which the abnormal track belongs, screening out a target detection frame with the highest score, and extracting feature information of the road target from the target detection frame. According to the invention, the speed and precision of road target identification in a complex environment are improved, an abnormal analysis and subsequent processing method of the moving track is provided, the quality of road target monitoring is improved, and a data basis is provided for road management.

The invention relates to the technical field of video anomaly detection, and comprises a detection method based on multi-target tracking, and the method comprises the steps: employing a target detection and target tracking algorithm to carry out the precise detection and tracking of a vehicle in video data; according to the detection method based on the weak supervision video, vehicle abnormal behavior detection based on a video abnormal detection algorithm is adopted; according to the track classification-based detection method, a motion track-based detection algorithm is adopted to realize detection of vehicles with abnormal tracks in a monitoring video, and dichotomy of motion tracks of all vehicles in the monitoring video is realized through the track anomaly-based detection algorithm. Through combination of multiple detection modes, target detection tracking and lane line detection are combined to realize detection of vehicle positions and motion states, a two-stage network is provided, and accurate detection of abnormal events of highway vehicles is realized.

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 discloses an unmanned aerial vehicle control method and device for aloft work. The method comprises the following steps: when an unmanned aerial vehicle lifts to a preset height, acquiring an operation video of an operation site through a shooting device configured by the unmanned aerial vehicle, and marking a target object in the operation video by using a detection frame; tracking the target object based on a preset target tracking algorithm and the labeling box to obtain an object position of the target object; and determining a target angle of a pan-tilt of the unmanned aerial vehicle according to the object position through a proportional integral differential controller, and adjusting the pan-tilt to the target angle, so that the target object is located at the central position of the video frame of the target video, thereby solving the problem that the unmanned aerial vehicle cannot automatically and stably track the target in a working site, and improving the working efficiency of the unmanned aerial vehicle. The tracking precision and the video stability are improved, the manual intervention is reduced, and the working efficiency and the safety are enhanced.

The invention discloses a target tracking method for a hyperspectral video. The method comprises the following steps: initializing weight parameters in a hyperspectral target tracking algorithm; reading an initial frame of a tracking video as an initial template of target tracking and an online updating template image, and reading position information of a target in the initial frame; reading the next frame of the tracking video and cutting the next frame as a search image for target tracking; an initial template, an online updated template image and a search image serve as input of a hyperspectral target tracking algorithm, a three-channel image is obtained through dimensionality reduction of a dimensionality reduction module, then the three-channel image is input into a feature extraction network after low-rank adaptive learning, obtained feature information is input into a target position decoder, and a target position is obtained. Outputting the predicted position of the target in the current frame and the confidence of the target, and judging whether to update the online update template image or not according to the confidence; continuously repeating each frame of the tracked video and recording the predicted position of the target in each frame of the video; the method has the advantages of high target tracking accuracy, strong extraction feature representation, low training cost and the like.

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 a television infrared composite image target tracking method and system based on an FPGA (Field Programmable Gate Array). The method comprises the following steps: acquiring television and infrared double-channel video stream images, cross-clock domain synchronous frame frequency and size in real time; hLS high-level synthesis is carried out, a television infrared multimode feature enhancement converter tracking model is realized, feature extraction, fusion and prediction of multimode images are completed, and a target tracking result is obtained; superposing a tracking frame, and outputting a video stream in real time; according to the invention, the problems of low tracking precision, poor robustness, slow operation speed and difficulty in real-time tracking of an edge device end target tracking algorithm in complex and diverse environments such as illumination variation, night environment and target shielding in the prior art can be solved.

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.