Local sparse representation object tracking method based on LO regularization

Abstract

The invention discloses a local sparse representation object tracking method based on LO regularization. Different from traditional L1 object tracking methods, the method herein proposes the combination between a LO norm and a structural local sparse appearance model, fully utilizes sparse coding, better differentiates objects from backgrounds in the course of tracking, and models local shielding and the like interferences by using a trivial template, and further increases the robustness of noise interference in the course of tracking. In order to enable an object model to better deal with continuous changes of the appearance of an object in the course of tracking, according to the invention, the method, through the construction of an object model set, adopts the LO norm in reconstructing the object in the course of tracking and adopts probabilistic policy in replacing a certain template in the object template set with a reconstructing result, thus realizing the update of template dynamics and further increasing the stability of the algorithm. According to the invention, the method which is directed at the NP problem of the object function optimization solution based on the LO norm, employs the APG algorithm in realizing effective solution.

Description

Technical field: [0001] The invention belongs to the field of machine vision, in particular to a local sparse representation target tracking method based on L0 regularization. Background technique: [0002] Object tracking technology is a research hotspot in the field of machine vision, which aims to continuously and accurately locate any object in the video, so as to complete more advanced visual tasks such as analysis and understanding of the target behavior. It has important research significance and broad application prospects in military international and civilian security. In recent years, researchers from various countries have proposed many methods and made a lot of progress in this field. But designing a robust object tracking algorithm is still a great challenge. Because there is illumination in the process of target tracking, occlusion and changes in the background of the target will have adverse effects on the tracking of the target. At the same time, in the p...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problem of decreased precision in the tracking method based on L1 norm regularized target coefficients, the present invention introduces L0 norm tracking, and at the same time uses trivial templates to model local occlusion and other heterogeneous interferences when modeling targets, and proposes a method based on Local sparse representation object tracking algorithm with L0 regularization for robust object tracking

Images