PVT++: A Simple End-to-End Latency-Aware Visual Tracking Framework

• URL: http://arxiv.org/abs/2211.11629v3
• Date: Mon, 17 Jul 2023 03:33:14 GMT
• Title: PVT++: A Simple End-to-End Latency-Aware Visual Tracking Framework
• Authors: Bowen Li, Ziyuan Huang, Junjie Ye, Yiming Li, Sebastian Scherer, Hang Zhao, Changhong Fu
• Abstract summary: We present a framework for end-to-end latency-aware tracking, i.e., end-to-end predictive visual tracking (PVT++) Unlike existing solutions that naively append Kalman Filters after trackers, PVT++ can be jointly optimized. PVT++ can achieve significant performance gain on various trackers and exhibit higher accuracy than prior solutions.
• Score: 33.7932898514321
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual object tracking is essential to intelligent robots. Most existing approaches have ignored the online latency that can cause severe performance degradation during real-world processing. Especially for unmanned aerial vehicles (UAVs), where robust tracking is more challenging and onboard computation is limited, the latency issue can be fatal. In this work, we present a simple framework for end-to-end latency-aware tracking, i.e., end-to-end predictive visual tracking (PVT++). Unlike existing solutions that naively append Kalman Filters after trackers, PVT++ can be jointly optimized, so that it takes not only motion information but can also leverage the rich visual knowledge in most pre-trained tracker models for robust prediction. Besides, to bridge the training-evaluation domain gap, we propose a relative motion factor, empowering PVT++ to generalize to the challenging and complex UAV tracking scenes. These careful designs have made the small-capacity lightweight PVT++ a widely effective solution. Additionally, this work presents an extended latency-aware evaluation benchmark for assessing an any-speed tracker in the online setting. Empirical results on a robotic platform from the aerial perspective show that PVT++ can achieve significant performance gain on various trackers and exhibit higher accuracy than prior solutions, largely mitigating the degradation brought by latency.

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