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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 30 / No. 1: Thirtieth AAAI Conference On Artificial Intelligence

MC-HOG Correlation Tracking with Saliency Proposal

March 8, 2023

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Authors

Guibo Zhu

Institute of Automation, Chinese Academy of Sciences


Jinqiao Wang

Institute of Automation, Chinese Academy of Sciences


Yi Wu

Nanjing University of Information Science and Technology


Xiaoyu Zhang

Chinese Academy of Sciences


Hanqing Lu

Institute of Automation, Chinese Academy of Sciences


DOI:

10.1609/aaai.v30i1.10450


Abstract:

Designing effective feature and handling the model drift problem are two important aspects for online visual tracking. For feature representation, gradient and color features are most widely used, but how to effectively combine them for visual tracking is still an open problem. In this paper, we propose a rich feature descriptor, MC-HOG, by leveraging rich gradient information across multiple color channels or spaces. Then MC-HOG features are embedded into the correlation tracking framework to estimate the state of the target. For handling the model drift problem caused by occlusion or distracter, we propose saliency proposals as prior information to provide candidates and reduce background interference. In addition to saliency proposals, a ranking strategy is proposed to determine the importance of these proposals by exploiting the learnt appearance filter, historical preserved object samples and the distracting proposals. In this way, the proposed approach could effectively explore the color-gradient characteristics and alleviate the model drift problem. Extensive evaluations performed on the benchmark dataset show the superiority of the proposed method.

Topics: AAAI

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HOW TO CITE:

Guibo Zhu|| Jinqiao Wang|| Yi Wu|| Xiaoyu Zhang|| Hanqing Lu MC-HOG Correlation Tracking with Saliency Proposal Proceedings of the AAAI Conference on Artificial Intelligence, 30 (2016) .

Guibo Zhu|| Jinqiao Wang|| Yi Wu|| Xiaoyu Zhang|| Hanqing Lu MC-HOG Correlation Tracking with Saliency Proposal AAAI 2016, .

Guibo Zhu|| Jinqiao Wang|| Yi Wu|| Xiaoyu Zhang|| Hanqing Lu (2016). MC-HOG Correlation Tracking with Saliency Proposal. Proceedings of the AAAI Conference on Artificial Intelligence, 30, .

Guibo Zhu|| Jinqiao Wang|| Yi Wu|| Xiaoyu Zhang|| Hanqing Lu. MC-HOG Correlation Tracking with Saliency Proposal. Proceedings of the AAAI Conference on Artificial Intelligence, 30 2016 p..

Guibo Zhu|| Jinqiao Wang|| Yi Wu|| Xiaoyu Zhang|| Hanqing Lu. 2016. MC-HOG Correlation Tracking with Saliency Proposal. "Proceedings of the AAAI Conference on Artificial Intelligence, 30". .

Guibo Zhu|| Jinqiao Wang|| Yi Wu|| Xiaoyu Zhang|| Hanqing Lu. (2016) "MC-HOG Correlation Tracking with Saliency Proposal", Proceedings of the AAAI Conference on Artificial Intelligence, 30, p.

Guibo Zhu|| Jinqiao Wang|| Yi Wu|| Xiaoyu Zhang|| Hanqing Lu, "MC-HOG Correlation Tracking with Saliency Proposal", AAAI, p., 2016.

Guibo Zhu|| Jinqiao Wang|| Yi Wu|| Xiaoyu Zhang|| Hanqing Lu. "MC-HOG Correlation Tracking with Saliency Proposal". Proceedings of the AAAI Conference on Artificial Intelligence, 30, 2016, p..

Guibo Zhu|| Jinqiao Wang|| Yi Wu|| Xiaoyu Zhang|| Hanqing Lu. "MC-HOG Correlation Tracking with Saliency Proposal". Proceedings of the AAAI Conference on Artificial Intelligence, 30, (2016): .

Guibo Zhu|| Jinqiao Wang|| Yi Wu|| Xiaoyu Zhang|| Hanqing Lu. MC-HOG Correlation Tracking with Saliency Proposal. AAAI[Internet]. 2016[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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