Abstract
The tracking algorithm of compressed sensing takes advantage of the objective’s background information, but lacks the feedback mechanism towards the results. The 11 sparse tracking algorithm adapts to the changes in the objectives’ appearances but at the cost of losing their background information. To enhance the effectiveness and robustness of the algorithm in coping with such distractions as occlusion and illumination variation, this paper proposes a tracking framework with the 11 sparse representation being the detector and compressed sensing algorithm the tracker, and establishes a complementary classifier model. A second-order model updating strategy has therefore been proposed to preserve the most representative templates in the 11 sparse representations. It is concluded that this tracking algorithm is better than the prevalent 8 ones with a respective precision plot of 77.15 %, 72.33 % and 81.13 % and a respective success plot of 77.67 %, 74.01 %, 81.51 % in terms of the overall, occlusion and illumination variation.
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Sun, H., Li, J., Du, B., Tao, D. (2016). On Combining Compressed Sensing and Sparse Representations for Object Tracking. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9916. Springer, Cham. https://doi.org/10.1007/978-3-319-48890-5_4
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