A Robust Tracking with Low-Dimensional Target-Specific Feature Extraction

Chengcheng JIANG  Xinyu ZHU  Chao LI  Gengsheng CHEN  

IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.7   pp.1349-1361
Publication Date: 2019/07/01
Publicized: 2019/04/19
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2019EDP7032
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
object tracking,  feature extraction,  PCA,  CNN,  particle filter,  

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Pre-trained CNNs on ImageNet have been widely used in object tracking for feature extraction. However, due to the domain mismatch between image classification and object tracking, the submergence of the target-specific features by noise largely decreases the expression ability of the convolutional features, resulting in an inefficient tracking. In this paper, we propose a robust tracking algorithm with low-dimensional target-specific feature extraction. First, a novel cascaded PCA module is proposed to have an explicit extraction of the low-dimensional target-specific features, which makes the new appearance model more effective and efficient. Next, a fast particle filter process is raised to further accelerate the whole tracking pipeline by sharing convolutional computation with a ROI-Align layer. Moreover, a classification-score guided scheme is used to update the appearance model for adapting to target variations while at the same time avoiding the model drift that caused by the object occlusion. Experimental results on OTB100 and Temple Color128 show that, the proposed algorithm has achieved a superior performance among real-time trackers. Besides, our algorithm is competitive with the state-of-the-art trackers in precision while runs at a real-time speed.