Patch-based object tracking using the local robust histogram and background estimation
Ruitao Lu, Wanying Xu, Yongbin Zheng, Shengjian Bai, Xinsheng Huang
Available Online January 2016.
- https://doi.org/10.2991/ifmeita-16.2016.135How to use a DOI?
- Object tracking; Image segmentation; background estimation.
- A novel patch-based algorithm for robust object tracking is proposed in this study. The patches of the appearance model are represented by the proposed local robust histogram. Then, the background model is constructed by a set of new spatial probability maps in a surrounding â€œcontext windowâ€. For a new testing frame, the vote maps that are obtained by matching the target patches independently are fused for determining the new location of the object. Then, a two-stage estimation method is proposed to estimate the probability of the pixels belonging to the target in the new location. The patches are classified into foreground patches and occluded patches. At last, a dynamic updating scheme is proposed to address appearance variations and alleviate tracking drift. Experiments and evaluations on various challenging image sequences are performed, and the results show that the proposed algorithm performs favorably against other state-of-the-art methods.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Ruitao Lu AU - Wanying Xu AU - Yongbin Zheng AU - Shengjian Bai AU - Xinsheng Huang PY - 2016/01 DA - 2016/01 TI - Patch-based object tracking using the local robust histogram and background estimation BT - 2016 International Forum on Management, Education and Information Technology Application PB - Atlantis Press SN - 2352-5398 UR - https://doi.org/10.2991/ifmeita-16.2016.135 DO - https://doi.org/10.2991/ifmeita-16.2016.135 ID - Lu2016/01 ER -