Anti-Occlusion Tracking Method Based on Kernel Correlation Filter

DOI

10.2991/ncce-18.2018.77How to use a DOI?

Keywords

kernelized correlation filters; target tracking; classifier response; occlusion detection; target search.

Abstract

In the real scene, the target itself and the background will be unpredictable changes, such as the occlusion and other factors will bring greater challenges to the object tracking. In this paper, we proposed a method that handle the occlusion based on Kernel Correlation Filters (KCFFR, KCF with forward response). An anti-occlusion model is established based on the distribution feature of the forward classifier in response to the maximum value of the forward classifier. The target is searched by the block region helix search method after the target is occluded, and the response of the sliding box is calculated in the target search process to determine whether the target is found. The algorithm is tested on the OTB (Object Tracking Benchmark) sequence and compared with the four tracking algorithms. The results show the effectiveness and accuracy of the KCFFR algorithm

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Weichuang Jiang
AU  - Fuxing Zhang
PY  - 2018/05
DA  - 2018/05
TI  - Anti-Occlusion Tracking Method Based on Kernel Correlation Filter
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 492
EP  - 495
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.77
DO  - 10.2991/ncce-18.2018.77
ID  - Jiang2018/05
ER  -