Adaptive simultaneous sparse representation-based robust target tracking method

Abstract

The invention discloses an adaptive simultaneous sparse representation-based robust target tracking method. The method comprises the following steps of S1, according to the value of Laplacian noise energy, adaptively building a simultaneous sparse tracking model; S2, solving the built tracking model; and S3, updating a template. The tracking method provided by the invention is good in tracking identification effect and relatively good in interference resistance, can realize relatively accurate and real-time target tracking, and is relatively stable.

Description

Technical field [0001] The invention belongs to the technical field of computer image processing, and relates to a target tracking method, and more specifically, to a robust target tracking method based on adaptive simultaneous sparse representation. Background technique [0002] Target tracking occupies an important position in the field of computer vision. With the use of high-quality computers and cameras and the need for automatic video analysis, people are interested in target tracking. The main tasks of target tracking include: detection of moving targets of interest, continuous tracking from frame to frame, and behavior analysis of tracking targets. Currently, relevant applications of target tracking include: motion recognition, video retrieval, human-computer interaction, traffic monitoring, car navigation, etc. [0003] Currently, although many tracking algorithms have been proposed, target tracking technology still faces many challenges. In the actual tracking process, ...

AI Technical Summary

Problems solved by technology

Although the discriminant method and the generative method have achieved reliable tracking to a certain extent, they also have their own shortcomings. First, the discriminant method has high requirements for feature extraction, so it is sensitive to noise in the actual tracking process, and it may appear for noisy targets. Tracking fails, and the generation method cannot accurately find areas similar to the target in the mixed background, so it is prone to tracking failure; second, the discriminant method needs sufficient training sample sets, good samples can improve the performance of the classifier, and bad samples It will weaken the performance of the classifier. If bad samples are introduced into the classifier, the tracking effect will be affected, and the generation method is more sensitive to the template. Once the occluded target is introduced into the template by mistake, tracking failure may occur. Therefore, the two methods are in the real scene. are not sufficiently robust in tracking

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