Robust Object Tracking Method Based on Adaptive Simultaneous Sparse Representation

Abstract

The robust target tracking method based on adaptive simultaneous sparse representation of the present invention comprises the following steps: S1, according to the size of Laplacian noise energy, adaptively establishes a simultaneous sparse tracking model; S2, to the established tracking model Solving; S3, updating the template. The tracking method provided by the invention has good tracking and recognition effect, strong anti-interference ability, can realize more accurate and real-time target tracking, and the tracking method 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, in particular to a robust target tracking method based on adaptive simultaneous sparse representation. Background technique [0002] Object 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 object tracking. The main tasks of target tracking include: detection of moving targets of interest, continuous tracking between video frames and behavior analysis of tracked targets. Currently, related applications of object tracking include: motion recognition, video retrieval, human-computer interaction, traffic monitoring, vehicle navigation, etc. [0003] At present, although many tracking algorithms have been proposed, the target tracking technology still faces many challenges. In the actual tracking proce...

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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