Learning attribute-specific representations for visual tracking

Yuankai Qi, Shengping Zhang, Weigang Zhang, Li Su, Qingming Huang, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Citations (Scopus)

Abstract

In recent years, convolutional neural networks (CNNs) have achieved great success in visual tracking. Most of existing methods train or fine-tune a binary classifier to distinguish the target from its background. However, they may suffer from the performance degradation due to insufficient training data. In this paper, we show that attribute information (e.g., illumination changes, occlusion and motion) in the context facilitates training an effective classifier for visual tracking. In particular, we design an attribute-based CNN with multiple branches, where each branch is responsible for classifying the target under a specific attribute. Such a design reduces the appearance diversity of the target under each attribute and thus requires less data to train the model. We combine all attribute-specific features via ensemble layers to obtain more discriminative representations for the final target/background classification. The proposed method achieves favorable performance on the OTB100 dataset compared to state-of-the-art tracking methods. After being trained on the VOT datasets, the proposed network also shows a good generalization ability on the UAV-Traffic dataset, which has significantly different attributes and target appearances with the VOT datasets.

Original languageEnglish
Title of host publication33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019
PublisherAAAI press
Pages8835-8842
Number of pages8
ISBN (Electronic)9781577358091
Publication statusPublished - 2019
Event33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 - Honolulu, United States
Duration: 2019 Jan 272019 Feb 1

Publication series

Name33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019

Conference

Conference33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019
Country/TerritoryUnited States
CityHonolulu
Period19/1/2719/2/1

Bibliographical note

Funding Information:
This work was supported in part by National Natural Science Foundation of China: 61620106009, 61332016, U1636214, 61872112, 61472389 and 61672497, Key Research Program of Frontier Sciences, CAS: QYZDJ-SSW-SYS013, Shandong Provincial Natural Science Foundation, China: ZR2017MF001, the NSF CAREER Grant (No. 1149783), and gifts from Adobe and Nvidia.

Publisher Copyright:
© 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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