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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Multi-Task Driven Feature Models for Thermal Infrared Tracking

February 1, 2023

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Authors

Qiao Liu

Harbin Institute of Technology, Shenzhen


Xin Li

Harbin Institute of Technology, Shenzhen


Zhenyu He

Harbin Institute of Technology, Shenzhen


Nana Fan

Harbin Institute of Technology, Shenzhen


Di Yuan

Harbin Institute of Technology, Shenzhen


Wei Liu

Shenzhen Institute of Information Technology


Yongsheng Liang

Harbin Institute of Technology, Shenzhen


DOI:

10.1609/aaai.v34i07.6828


Abstract:

Existing deep Thermal InfraRed (TIR) trackers usually use the feature models of RGB trackers for representation. However, these feature models learned on RGB images are neither effective in representing TIR objects nor taking fine-grained TIR information into consideration. To this end, we develop a multi-task framework to learn the TIR-specific discriminative features and fine-grained correlation features for TIR tracking. Specifically, we first use an auxiliary classification network to guide the generation of TIR-specific discriminative features for distinguishing the TIR objects belonging to different classes. Second, we design a fine-grained aware module to capture more subtle information for distinguishing the TIR objects belonging to the same class. These two kinds of features complement each other and recognize TIR objects in the levels of inter-class and intra-class respectively. These two feature models are learned using a multi-task matching framework and are jointly optimized on the TIR tracking task. In addition, we develop a large-scale TIR training dataset to train the network for adapting the model to the TIR domain. Extensive experimental results on three benchmarks show that the proposed algorithm achieves a relative gain of 10% over the baseline and performs favorably against the state-of-the-art methods. Codes and the proposed TIR dataset are available at https://github.com/QiaoLiuHit/MMNet.

Topics: AAAI

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HOW TO CITE:

Qiao Liu||Xin Li||Zhenyu He||Nana Fan||Di Yuan||Wei Liu||Yongsheng Liang Multi-Task Driven Feature Models for Thermal Infrared Tracking Proceedings of the AAAI Conference on Artificial Intelligence (2020) 11604-11611.

Qiao Liu||Xin Li||Zhenyu He||Nana Fan||Di Yuan||Wei Liu||Yongsheng Liang Multi-Task Driven Feature Models for Thermal Infrared Tracking AAAI 2020, 11604-11611.

Qiao Liu||Xin Li||Zhenyu He||Nana Fan||Di Yuan||Wei Liu||Yongsheng Liang (2020). Multi-Task Driven Feature Models for Thermal Infrared Tracking. Proceedings of the AAAI Conference on Artificial Intelligence, 11604-11611.

Qiao Liu||Xin Li||Zhenyu He||Nana Fan||Di Yuan||Wei Liu||Yongsheng Liang. Multi-Task Driven Feature Models for Thermal Infrared Tracking. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.11604-11611.

Qiao Liu||Xin Li||Zhenyu He||Nana Fan||Di Yuan||Wei Liu||Yongsheng Liang. 2020. Multi-Task Driven Feature Models for Thermal Infrared Tracking. "Proceedings of the AAAI Conference on Artificial Intelligence". 11604-11611.

Qiao Liu||Xin Li||Zhenyu He||Nana Fan||Di Yuan||Wei Liu||Yongsheng Liang. (2020) "Multi-Task Driven Feature Models for Thermal Infrared Tracking", Proceedings of the AAAI Conference on Artificial Intelligence, p.11604-11611

Qiao Liu||Xin Li||Zhenyu He||Nana Fan||Di Yuan||Wei Liu||Yongsheng Liang, "Multi-Task Driven Feature Models for Thermal Infrared Tracking", AAAI, p.11604-11611, 2020.

Qiao Liu||Xin Li||Zhenyu He||Nana Fan||Di Yuan||Wei Liu||Yongsheng Liang. "Multi-Task Driven Feature Models for Thermal Infrared Tracking". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.11604-11611.

Qiao Liu||Xin Li||Zhenyu He||Nana Fan||Di Yuan||Wei Liu||Yongsheng Liang. "Multi-Task Driven Feature Models for Thermal Infrared Tracking". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 11604-11611.

Qiao Liu||Xin Li||Zhenyu He||Nana Fan||Di Yuan||Wei Liu||Yongsheng Liang. Multi-Task Driven Feature Models for Thermal Infrared Tracking. AAAI[Internet]. 2020[cited 2023]; 11604-11611.


ISSN: 2374-3468


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101, Palo Alto, California 94303 All Rights Reserved

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