Target tracking method based on sparse representation

Abstract

The invention relates to the target tracking technology and discloses a target tracking method based on sparse representation. According to the technical scheme, sparse learning is conducted on obstructions by means of the spatial continuity and prior information of the obstructions, the sparse coefficient is obtained by means of an updated sparse representing model on this basis, the reconstitution residual error is calculated according to the obtained sparse coefficient, real-time updating is conducted on a redundant dictionary with the minimum target of the reconstitution residual error as a tracking target, the position of the target at the next moment is predicted with the particle filter tracking method, an estimation target is obtained, and finally the obtained estimation target and the updated redundant dictionary are fed back to the sparse representing model to conduct repeated iteration. According to the method, the sparse learning idea is introduced to the particle filter tracking algorithm based on sparse representation, sparse learning of the obstructions and establishment of an obstruction model can be conducted under the condition that the obstructions are not sparse, and accurate tracking of the target can be conducted according to the updated sparse representation model.

Description

technical field [0001] The invention relates to target tracking technology, in particular to a sparse representation-based particle filter tracking method for occluded target tracking. Background technique [0002] In the target tracking technology, the traditional target tracking method has the Mean-Shift tracking algorithm based on target pattern search and matching (see Comaniciu D, Ramesh V, Meer P. Kernel-Based Object Tacking. IEEE Trans on Pattern Analysis and Machine Intelligence, 2003 ,25(5):564-577), classification-based Boosting target tracking algorithm (see] Avidan S.Ensemble Tracking.IEEE Trans on Pattern Analysis and Machine Intelligence, 2007, 29(2):261-271), based on Carl Mann filtering tracking algorithm (see Yim J, Jeong S, Gwon K, et al. Improvement of Kalman Filters for WLAN Based Indoor Tracking. Expert Systems with Applications, 2010, 37(1): 426-433) and particle filter-based Tracking algorithm (see Wang Zhaowen, Yang Xiaokang, Xu Yi, et al. CamShift G...

AI Technical Summary

Problems solved by technology

However, the premise of this type of method is that the occlusion that causes the target change is sparse. However, this sparsity assumption is not true in all cases. It is difficult for the existing technology to track the target when the occlusion is not sparse.

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