A Robust Object Tracking Method Based on Locally Discriminative Sparse Representations

Abstract

The present invention discloses a robustness object tracking method based on local identification sparse representation. The method comprises: firstly, cutting out a plurality of different templates of an object to be tracked from a first frame image, performing blocking of each template, and taking the subblock located at the same corresponding position in all the templates as a sample type; secondary, performing feature extraction of each image block by using the HOG feature extraction method, and performing identification dictionary learning by using an LC-KSVD algorithm; thirdly, adding the local identification sparse representation model to a mean drift frame to predicate the position where the object is located; and finally, in order to overcome the change of the object appearance in the tracking process, a dictionary on-line update method is provided to realize the persistence modeling of the object. The robustness object tracking method based on local identification sparse representation not only employs the features of the object image itself but also introduce the identification information between different types of images to perform modeling of the object appearance for realize the object tracking, and therefore the robustness object tracking method based on local identification sparse representation has better robustness.

Description

technical field [0001] The invention relates to a method for effectively and robustly tracking a target in a video sequence when the target pose changes, is interfered by background clutter, and the target is partially occluded, and belongs to the technical field of computer vision. Background technique [0002] Object tracking is an important research content in the field of computer vision, and it can make important contributions to many application fields such as video surveillance, human-computer interaction, vehicle navigation, and robotics. Although many researchers are conducting in-depth research and exploration on this problem, the influence of interference factors such as changes in the appearance and scale of the target, illumination, background clutter, and partial occlusion of the target make the existing target tracking algorithms still unable to achieve the desired results. satisfactory effect. [0003] In recent years, with the continuous development of spar...

AI Technical Summary

Problems solved by technology

[0008] (1) When the target to be tracked is partially occluded for a long time or the appearance of the target changes significantly, it is usually impossible to model the target well, causing the tracker to drift away from the target;

[0009] (2) When the background clutter around the target is strong, many algorithms cannot accurately model the target due to the interference of the background clutter, resulting in unsatisfactory tracking results

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