Release the power of online-training for robust visual tracking

Yifan Yang, Guorong Li*, Yuankai Qi, Qingming Huang*

*Corresponding author for this work

Research output: Contribution to journalConference paperpeer-review

17 Citations (Scopus)

Abstract

Convolutional neural networks (CNNs) have been widely adopted in the visual tracking community, significantly improving the state-of-the-art. However, most of them ignore the important cues lying in the distribution of training data and high-level features that are tightly coupled with the target/background classification. In this paper, we propose to improve the tracking accuracy via online training. On the one hand, we squeeze redundant training data by analyzing the dataset distribution in low-level feature space. On the other hand, we design statistic-based losses to increase the inter-class distance while decreasing the intra-class variance of high-level semantic features. We demonstrate the effectiveness on top of two high-performance tracking methods: MDNet and DAT. Experimental results on the challenging large-scale OTB2015 and UAVDT demonstrate the outstanding performance of our tracking method.

Original languageEnglish
Pages (from-to)12645-12652
Number of pages8
JournalProceedings of the AAAI Conference on Artificial Intelligence
Volume34
Issue number07
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event34th AAAI Conference on Artificial Intelligence, AAAI 2020 - New York, United States
Duration: 7 Feb 202012 Feb 2020

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