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CFTracker: Object Tracking by Channel Filtering

Published: 28 June 2024 Publication History

Abstract

In visual object tracking, the background noise information in the target scene is one of the main challenging factors affecting the tracking performance, while the existing trackers do not make full use of the background information which causes tracking drift of the trackers when facing complex background noise (such as occlusion and the background changes). To address this issue, in this work, we propose a novel channel filtering module(CFM) that leverages global context information of frames to assist in tracking. The proposed method effectively reduces the interference of background information by employing an improved CFM. Specifically, our approach consists of a channel processing module and a spatial filtering module. Finally, we propose a tracking framework, called CFTracker, which incorporates CFM to assist in tracking. This framework enhances the discriminability between the target and the background, thereby improving tracking performance. Experimental results demonstrate the promising performance of our CFTracker on four challenging datasets. Furthermore, our method achieves improved tracking performance without significantly increasing the computational overhead of the tracking network.

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    ICRSA '23: Proceedings of the 2023 6th International Conference on Robot Systems and Applications
    September 2023
    335 pages
    ISBN:9798400708039
    DOI:10.1145/3655532
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 28 June 2024

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