Hedged Deep Tracking
Yuankai Qi, Shengping Zhang, Lei Qin, Hongxun Yao, Qingming Huang, Jongwoo Lim, Ming-Hsuan Yang
Abstract
In recent years, several methods have been developed to utilize hierarchical features learned from a deep convolutional neural network (CNN) for visual tracking. However, as the features from a certain CNN layer characterize an object of interest from only one aspect or one level, the performance of such trackers trained with features from one layer (usually the last second layer) can be further improved. In this paper, we propose a novel CNN based tracking framework, which takes full advantage of features from different CNN layers and uses an adaptive Hedge method to hedge several CNN trackers into a stronger one. Extensive experiments on a benchmark dataset of 100 challenging image sequences demonstrate the effectiveness of the proposed algorithm compared with several state-of-the-art trackers.
BibTeX
@inproceedings{cvpr2016_hedgeddeeptracki,
title = {Hedged Deep Tracking},
author = {Yuankai Qi and Shengping Zhang and Lei Qin and Hongxun Yao and Qingming Huang and Jongwoo Lim and Ming-Hsuan Yang},
booktitle = {CVPR 2016},
year = {2016}
}