3results about How to "Accurate Target Tracking" patented technology

The invention relates to image processing and video monitoring, in particular to a real-time target tracking method and system based on double-camera linkage, and the method comprises the steps: initializing the system and defining a coordinate system; when the target enters the high-definition picture of the high-definition zoom camera, judging whether the target enters the field of view of the wide-angle fixed-focus camera or not in real time; if the target enters the view field of the wide-angle fixed-focus camera, guiding the high-definition zoom camera to turn to the target by using the large view field advantage of the wide angle, and carrying out picture fusion and asymmetric weight ROI video coding; if the target does not enter the field of view of the wide-angle fixed-focus camera, starting an independent closed-loop PID tracking mode for the high-definition zoom camera, and gradually switching the high-definition zoom camera from the wide-angle end to the long-focus end to realize stable locking of the target in a full-focus range; according to the technical scheme provided by the invention, the defect that the moving target is difficult to accurately track in the prior art can be effectively overcome.

The invention discloses a multi-source track data fusion method and system based on an LSTM network, and is applied to the technical field of multi-source track fusion. Comprising the following steps of performing time registration on track points, constructing an input sequence of LSTM, calculating correlation probability between radar original track points and existing fusion track points through the LSTM, classifying the radar original track points, performing quality management on a track classification result based on an average correlation probability, and performing quality management on the track classification result by fusing data of a single target and a plurality of radars. A future state of the target is estimated using a filtering algorithm. According to the method, the LSTM algorithm is introduced, the contextual information of the time sequence data is used for capturing the continuity of the target in time and the relevance of the target in space, the accuracy of track relevance is effectively improved, the model is used for track identity judgment and track quality management, and therefore the performance of a track fusion system is improved.

This invention provides a deep learning-based method for multi-target pedestrian tracking, relating to the field of computer vision technology. The method includes: S1 acquiring video data, extracting frames from the video data to convert them into image frames, and preprocessing the image frames; S2 using an improved YOLOv5-s model to detect pedestrian targets in the image frames, obtaining the pedestrian targets; S3 using an improved Strong SORT model to track the pedestrian targets in each frame, assigning an ID to each tracked pedestrian, and generating pedestrian tracking results. This invention can accurately locate each pedestrian in a video and track their trajectory.