Target tracking algorithm based on self-adaptive particle filter and sparse representation

Abstract

The invention provides a target tracking algorithm based on the self-adaptive particle filter and sparse representation. According to the target tracking algorithm based on the self-adaptive particle filter and the sparse representation, the improved self-adaptive particle filter technique is adopted to serve as a tracking algorithm framework, a block sparse representation model is used for establishing an observation similarity model of a target, partitioning of the target is achieved by means of the self-adaptive partitioning technique, a structural sparse column diagram of a current target state is constructed to calculate the observation similarity of the current target state, blocking is detected by means of a blocking detection mechanism, a target / background dictionary template and a target template column diagram are updated to capture the change of the appearance of the target and the change of the environment during tracking, L1 optimization in the sparse representation is achieved by means of the variable-direction multiplicator method, and then the execution speed of the target tracking algorithm is increased. The target tracking algorithm based on the self-adaptive particle filter and the sparse representation has the advantage that the robustness to the conditions of the posture change of the tracking target, the change of the environment and lighting and blocking is strong.

Description

technical field [0001] The invention belongs to the field of computer vision, specifically relates to the field of intelligent monitoring, in particular to a target tracking algorithm in a complex environment based on an adaptive particle filter and a block sparse representation model. Background technique [0002] Vision-based object tracking plays an important role in many computer vision applications, such as robotics, video surveillance, and medical image analysis. Although vision-based object tracking technology has made some progress in recent decades, in some complex environments, such as complex backgrounds, drastic changes in illumination, and changes in the shape of objects, the existing visual tracking technology is still not very effective. Complete the tracking task well. Considering the complexity of the tracking task, how to design a tracking algorithm with strong robustness and good real-time performance is an important topic in the field of computer vision....

AI Technical Summary

Problems solved by technology

Since the algorithm is the final result based on a series of template libraries, it is robust to illumination changes, complex environments, and attitude changes, such as the literature "X. Mei and H. Ling. "Robust visual tracking using L1 minimization". 12th International Conference on Computer Vision, Kyoto, Japan, 2009(1436-1443). "However, because the template library often uses the overall template of the target as a feature, it is not effective in dealing with the occlusion of the target.

At the same time, when solving sparse coefficients, it is necessary to solve the L1 optimization problem. The specific solution requires a large amount of calculation, which often affects the actual use of the algorithm.

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