基于深度学习的鲁棒性视觉跟踪方法
Gao, Junyu; Yang, Xiaoshan; Zhang, Tianzhu; Xu, Changsheng
2016
发表期刊计算机学报
卷号39期号:7页码:1419-1434
其他摘要The traditional tracking methods(e.g. L1 tracker) generally adopt the pixel values as feature representation, and ignore the deep visual features of image patches. In a fixed video scene of the real world, we realize that we can usually find an area where the targets have clear appearance and are easy to distinguish. Therefore, in this paper, we select a region in each video to construct training set for deep model learning. In the proposed deep model, we design a deep convolutional neural network which has two symmetrical paths with the shared weights. The goal of the proposed deep network is to reduce the difference between the features of a target out of the region and in the region. As a result, the learned deep network can enhance the appearance feature of targets and benefit the trackers that utilize low-level feature, such as L1 tracker . Finally, we utilize this pre-trained deep convolutional network in the L1 tracker to extract features for sparse representation. Consequently, our method achieves the robustness in tracking for handling the challenges such as occlusion and illumination changes. We evaluate the proposed approach on 25 challenging videos against with 9 state-of-the-art trackers. The extensive results show that the proposed algorithm is 0.11 higher than the second best with average overlap, and is 1.0 lower than the second best with the average center location errors.
关键词深度学习 卷积神经网络 视觉跟踪 鲁棒性 L1跟踪系统 计算机视觉
语种中文
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/20480
专题模式识别国家重点实验室_多媒体计算与图形学
作者单位中国科学院自动化研究所模式识别国家重点实验室
推荐引用方式
GB/T 7714
Gao, Junyu,Yang, Xiaoshan,Zhang, Tianzhu,等. 基于深度学习的鲁棒性视觉跟踪方法[J]. 计算机学报,2016,39(7):1419-1434.
APA Gao, Junyu,Yang, Xiaoshan,Zhang, Tianzhu,&Xu, Changsheng.(2016).基于深度学习的鲁棒性视觉跟踪方法.计算机学报,39(7),1419-1434.
MLA Gao, Junyu,et al."基于深度学习的鲁棒性视觉跟踪方法".计算机学报 39.7(2016):1419-1434.
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