Long-Term Tracking Based on Multi-Feature Adaptive Fusion for Video Target

Hainan ZHANG  Yanjing SUN  Song LI  Wenjuan SHI  Chenglong FENG  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.5   pp.1342-1349
Publication Date: 2018/05/01
Publicized: 2018/02/02
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDP7245
Type of Manuscript: PAPER
Category: Fundamentals of Information Systems
target tracking,  correlation filter,  feature,  fusion,  

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The correlation filter-based trackers with an appearance model established by single feature have poor robustness to challenging video environment which includes factors such as occlusion, fast motion and out-of-view. In this paper, a long-term tracking algorithm based on multi-feature adaptive fusion for video target is presented. We design a robust appearance model by fusing powerful features including histogram of gradient, local binary pattern and color-naming at response map level to conquer the interference in the video. In addition, a random fern classifier is trained as re-detector to detect target when tracking failure occurs, so that long-term tracking is implemented. We evaluate our algorithm on large-scale benchmark datasets and the results show that the proposed algorithm have more accurate and more robust performance in complex video environment.