Abstract
Since occlusion still be a challenge for object tracking in RGB data. In this paper, we propose an RGBD single-object tracker that built upon the well-known base KCF tracker and exploit how the depth information fusing to handle partial and long-term occlusion. To divides tracking model into parts, the proposed tracker could detect and handle occlusion of each part separately. Despite the robustness in tracking with long-term occlusion, our part-based tracker provides an adaptively updating learning matrix. Experimental results are conducted on our dataset, which demonstrate that our tracker contains stability in long-term tracking.
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Acknowledgment
At the point of finishing this paper, we are grateful for the support by the ChaoYing Technology, Co, Ltd, Sichuan, China.
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Zhang, XF., Zeng, AP., Huang, S., Qing, M., Zhou, Y. (2018). An RGBD Tracker Based on KCF Adaptively Handling Long-Term Occlusion. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_13
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DOI: https://doi.org/10.1007/978-3-319-95933-7_13
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