Context-learning correlation filters for long-term visual tracking

Hong Zhang; Bo Rao; Heding Xu; Yifan Yang; Zeyu Zhang

doi:10.1117/12.2524187

6 May 2019 Context-learning correlation filters for long-term visual tracking

Hong Zhang, Bo Rao, Heding Xu, Yifan Yang, Zeyu Zhang

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 1106945 (2019) https://doi.org/10.1117/12.2524187
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

Abstract

Correlation Filters (CFs) based trackers have recently attracted many researchers’ attention because of their high efficiency and robustness. Nevertheless, CFs trackers usually require a cosine window on account of the boundary effects. This allows trackers to distinguish targets in small background areas. In this paper, we propose an online learning algorithm that employs the global context to alleviate the problems. It is based on Passive-Aggressive algorithm that incorporates context information within CFs trackers. In addition, we train an SVM classifier to redetect objects in case of the model drift caused by occlusion and fast motion etc. The results of extensive experiments on a large-scale benchmark dataset show that the proposed tracker outperform the state-of-the-art trackers.

Citation Download Citation

Hong Zhang, Bo Rao, Heding Xu, Yifan Yang, and Zeyu Zhang "Context-learning correlation filters for long-term visual tracking", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 1106945 (6 May 2019); https://doi.org/10.1117/12.2524187

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KEYWORDS

Image filtering

Detection and tracking algorithms

Target detection

Optical tracking

Motion models

Performance modeling

Visual process modeling

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