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515 results about "Video tracking" patented technology

Video tracking is the process of locating a moving object (or multiple objects) over time using a camera. It has a variety of uses, some of which are: human-computer interaction, security and surveillance, video communication and compression, augmented reality, traffic control, medical imaging and video editing. Video tracking can be a time consuming process due to the amount of data that is contained in video. Adding further to the complexity is the possible need to use object recognition techniques for tracking, a challenging problem in its own right.

Traffic violation detection method based on video tracking and pattern recognition

The invention discloses a traffic violation detection method based on video tracking and pattern recognition, and belongs to the technical fields of image recognition and pattern recognition. The method comprises the following steps: firstly, defining violation modes, namely defining traffic light stop lines, driving directions, and areas and directions of violation of course change; secondly, monitoring and controlling a camera to shoot video images of intersections; and thirdly, analyzing the video by an embedded system, performing detection and measurement by a pattern recognition method, using the video to track and acquire running tracks of vehicles, using the pattern recognition method to analyze behaviors of any one vehicle according to the running tracks, and immediately shooting the vehicle for recording if one of the following violation behaviors happens: going in wrong directions, violation of full line course change and running of a red light. A detection device used comprises a high-speed camera, a video controller, and a video tracking and pattern recognition device, wherein the video tracking and pattern recognition device comprises a traffic light signal receiving device, a video acquisition and conversion module, a violation setting module, a vehicle detection module, a video tracking module, a violation analysis module, a camera control module and a data wireless transmission module.
Owner:无锡乐骐科技股份有限公司

Video significance detecting method based on area segmentation

The invention discloses a video significance detecting method based on area segmentation, wherein the method mainly settles a problem of low detecting accuracy by an existing video significance detecting method. The video saliency detecting method comprises the steps of 1, performing linear iteration clustering on video frames, thereby obtaining a super-pixel block, and extracting the static characteristic of the super-pixel block; 2, by means of a variational optical flow method, obtaining the dynamic characteristic of the super-pixel block; 3, fusing the static characteristic and the dynamic characteristic for obtaining a characteristic matrix, and performing K-means clustering on the characteristic matrix; 4, performing linear regression model training on each cluster, thereby obtaining a regression model; and 5, reconstructing a mapping relation between a test set sample and a obtaining the significance value of a test set super-pixel block, and furthermore obtaining the significance graph of a testing sequence. Compared with a traditional video significance algorithm, the video significance detecting method has advantages of improving characteristic space and time representation capability, and reducing effect of illumination to detecting effect. The video significance detecting method can be used for early-period preprocessing of video target tracking and video segmenting.
Owner:XIDIAN UNIV

Multi-target tracking method based on graph representation and matching

The invention discloses a multi-target tracking method based on graph representation and matching. Compared with the prior art, the method has the advantage that the defect of incapability of successfully tracking due to frequent interactive shielding of targets and similar appearance features in a video tracking technology is overcome. The multi-target tracking method comprises the following steps: inputting a tracking video, and generating target-reliable short tracks in adjacent time windows; building a spatial motion model which takes a graph as a framework and an appearance model which takes color and local two-value difference as features for the formed target short tracks; calculating the appearance feature and spatial motion similarity among the tracks; realizing relevant tracking of a target by using a weighted two-value graph matching framework; repeating the steps continually to obtain a motion track at all moments of each target. Through adoption of the multi-target tracking method, the target tracking accuracy and efficiency in a complicated scene are increased, and the application degree of a track tracking technology in various scenes is increased. Accurate tracking of the target in a complicated environment is realized by means of online learning of the appearance model and the spatial motion model.
Owner:HEFEI UNIV OF TECH
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