Abstract
Online object tracking is a challenging problem because of changing environment including diverse illumination and occlusion conditions. The emergence of commercial real-time depth cameras like Kinect make online RGBD-based object tracking algorithm become a focus of research. In this paper, we propose a robust online depth image-based object tracking method with sparse coding. We introduce sigmoid normalization for local depth patch. In order to recovery from tracking failure in condition of heavily occlusion. we present a detection module based on PCA bases. Experiments show that our method exceeds original color image-based method in case of environment changes.
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Shen, SC., Zheng, WL., Lu, BL. (2014). Online Object Tracking Based on Depth Image with Sparse Coding. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8836. Springer, Cham. https://doi.org/10.1007/978-3-319-12643-2_29
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DOI: https://doi.org/10.1007/978-3-319-12643-2_29
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12642-5
Online ISBN: 978-3-319-12643-2
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