|Collaborative Learning Based on Convolutional Features and Correlation Filter for Visual Tracking
Suryo Adhi Wibowo, Hansoo Lee, Eun Kyeong Kim, and Sungshin Kim*
International Journal of Control, Automation, and Systems, vol. 16, no. 1, pp.335-349, 2018
Abstract : "One of the most important and challenging research topics in the area of computer vision is visual object
tracking, which is relevant to many real-world applications. Recently, discriminative correlation filters (DCF) have
been demonstrated to overcome the problems in visual object tracking efficiently. So far, only single-resolution
feature maps have been utilized in DCF. Owing to this limitation, the potential of DCF has not been exploited.
Moreover, convolutional features have demonstrated a better performance for visual tracking than histogram of
oriented gradients (HOG) features and color features. Based on these facts, in this paper, we propose collaborative
learning based on multi-resolution feature maps for DCF, employing convolutional features. Further, the confidence
score, which represents the location of the target object, is selected from various candidates based on certain rules.
In addition, the continuous filters are trained to handle the variations of appearance of the target. The extensive
experimental results obtained using VOT2015 and OTB-100 benchmark datasets show that the proposed algorithm
performs favorably against state-of-the-art tracking algorithms."
Collaborative learning, convolutional features, correlation filter, object tracking, visual tracking.