Abstract
Visual object tracking is widely researched but still challenging as both accuracy and efficiency must be considered in a single system. CT tracker can achieve a good real-time performance but is not very robust to fast movements. TLD framework has the ability to re-initialize object but can’t handle rotation and runs with low efficiency. In this paper, we propose a tracking algorithm combining the CT into TLD framework to overcome the disadvantages of each other. With the scale information obtained by an optical-flow tracker, we select samples for detector and use the detection result to correct the optical-flow tracker. The features are extracted using compressive sensing to improve the processing speed. The classifier parameters are updated by online learning. Considering the situation of continuous loss of object, a sliding window searching is also employed. Experiment results show that our proposed method achieves good performances in both precision and speed.
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The authors gratefully acknowledge financial support from China Scholarship Council.
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Yang, T., Cappelle, C., Ruichek, Y., El Bagdouri, M. (2017). Object Tracking Based on Modified TLD Framework Using Compressive Sensing Features. In: Sidorov, G., Herrera-Alcántara, O. (eds) Advances in Computational Intelligence. MICAI 2016. Lecture Notes in Computer Science(), vol 10061. Springer, Cham. https://doi.org/10.1007/978-3-319-62434-1_37
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DOI: https://doi.org/10.1007/978-3-319-62434-1_37
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