Object tracking: feature selection by reinforcement learning

Jiali Deng; Haigang Gong; Minghui Liu; Ming Liu

doi:10.1117/12.2626549

20 December 2021 Object tracking: feature selection by reinforcement learning

Jiali Deng, Haigang Gong, Minghui Liu, Ming Liu

Proceedings Volume 12155, International Conference on Computer Vision, Application, and Design (CVAD 2021); 121550I (2021) https://doi.org/10.1117/12.2626549
Event: International Conference on Computer Vision, Application, and Design (CVAD 2021), 2021, Sanya, China

Abstract

In recent years, deep convolutional features have been deployed in discriminative correlation filters (DCF) to boost object tracking performance. However, features captured from pre-trained classification networks are usually trained for image classification tasks, not object tracking. In this paper, we find that different convolutional feature channels play different roles in tracking different targets. Some feature channels are favorable for tracking a given target and can be acquired based on this target, some are irrelevant to track this target, and some can be the primary cause of trackers' performance degradation when tracking this target. Thus, we perform feature selection before learning correlation filters for object tracking, and the feature selection module is realized by reinforcement learning. We penalize the features non-positive to obtain a DCF tracker based on positive convolutional feature channels. Compared with DCF based trackers without a feature selection technique, our scheme improves the robustness of target representation, lessens the dimension of activations, and achieves better tracking performance. Extensive experiments on the OTB dataset demonstrate our feature selection scheme is simple, robust, and effective for DCF based trackers.

Citation Download Citation

Jiali Deng, Haigang Gong, Minghui Liu, and Ming Liu "Object tracking: feature selection by reinforcement learning", Proc. SPIE 12155, International Conference on Computer Vision, Application, and Design (CVAD 2021), 121550I (20 December 2021); https://doi.org/10.1117/12.2626549

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