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has been cited by the following article:

• TITLE: Underground Coal Mine Target Tracking via Multi-Feature Joint Sparse Representation

  AUTHORS: Yan Lu, Qingxiang Huang

  KEYWORDS: Underground Coal Mine, Sparse Representation, Particle Filter, Multi-Feature, Target-Tracking

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.3, March 31, 2021

  ABSTRACT: Single-feature methods are unable to effectively track a target in an underground coal mine video due to the high background noise, low and uneven illumination, and drastic light fluctuation in the video. In this study, we propose an underground coal mine personnel target tracking method using multi-feature joint sparse representation. First, with a particle filter framework, the global and local multiple features of the target template and candidate particles are extracted. Second, each of the candidate particles is sparsely represented by a dictionary template, and reconstruction is achieved after solving the sparse coefficient. Last, the particle with the lowest reconstruction error is deemed the tracking result. To validate the effectiveness of the proposed algorithm, we compare the proposed method with three commonly employed tracking algorithms. The results show that the proposed method is able to reliably track the target in various scenarios, such as occlusion and illumination change, which generates better tracking results and validates the feasibility and effectiveness of the proposed method.