title: Real-Time Visual Tracking through Fusion Features.
abstract: Due to their high-speed, correlation filters for object tracking have begun to receive increasing attention. Traditional object trackers based on correlation filters typically use a single type of feature. In this paper, we attempt to integrate multiple feature types to improve the performance, and we propose a new DD-HOG fusion feature that consists of discriminative descriptors (DDs) and histograms of oriented gradients (HOG). However, fusion features as multi-vector descriptors cannot be directly used in prior correlation filters. To overcome this difficulty, we propose a multi-vector correlation filter (MVCF) that can directly convolve with a multi-vector descriptor to obtain a single-channel response that indicates the location of an object. Experiments on the CVPR2013 tracking benchmark with the evaluation of state-of-the-art trackers show the effectiveness and speed of the proposed method. Moreover, we show that our MVCF tracker, which uses the DD-HOG descriptor, outperforms the structure-preserving object tracker (SPOT) in multi-object tracking because of its high-speed and ability to address heavy occlusion.
authors.affiliation: Key Laboratory of Precision Opto-Mechatronics Technology of Ministry of Education, Beihang University, Beijing 100191, China. ruanyang1987@163.com.
authors.forename: Yang
authors.initials: Y
authors.lastname: Ruan
authors.affiliation: Key Laboratory of Precision Opto-Mechatronics Technology of Ministry of Education, Beihang University, Beijing 100191, China. zhenzhongwei@buaa.edu.cn.
authors.forename: Zhenzhong
authors.initials: Z
authors.lastname: Wei
keywords: correlation filters; fusion feature; visual tracking