Real-Time Visual Tracking through Fusion Features
AbstractDue 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. View Full-Text
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Ruan, Y.; Wei, Z. Real-Time Visual Tracking through Fusion Features. Sensors 2016, 16, 949.
Ruan Y, Wei Z. Real-Time Visual Tracking through Fusion Features. Sensors. 2016; 16(7):949.Chicago/Turabian Style
Ruan, Yang; Wei, Zhenzhong. 2016. "Real-Time Visual Tracking through Fusion Features." Sensors 16, no. 7: 949.
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