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Robust Object Tracking Based on Self-adaptive Search Area

机译：基于自适应搜索区域的强大对象跟踪

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Discriminative correlation filter (DCF) based trackers have recently achieved excellent performance with great computational efficiency. However, DCF based trackers suffer boundary effects, which result in the unstable performance in challenging situations exhibiting fast motion. In this paper, we propose a novel method to mitigate this side-effect in DCF based trackers. We change the search area according to the prediction of target motion. When the object moves fast, broad search area could alleviate boundary effects and reserve the probability of locating object. When the object moves slowly, narrow search area could prevent effect of useless background information and improve computational efficiency to attain real-time performance. This strategy can impressively soothe boundary effects in situations exhibiting fast motion and motion blur, and it can be used in almost all DCF based trackers. The experiments on OTB benchmark show that the proposed framework improves the performance compared with the baseline trackers.
机译：基于识别的相关滤波器（DCF）的跟踪器最近具有卓越的计算效率。然而，基于DCF的跟踪器遭受边界效应，这导致呈现快速运动的具有挑战性的情况下的不稳定性能。在本文中，我们提出了一种新的方法来减轻基于DCF的跟踪器中的这种副作用。根据目标运动的预测，我们改变搜索区域。当对象快速移动时，广泛的搜索区域可以缓解边界效果并保留定位对象的概率。当物体缓慢移动时，狭窄的搜索区域可以防止无用的背景信息效果并提高计算效率以获得实时性能。该策略可以在表现出快速运动和运动模糊的情况下令人瞩目地令人估不舒缓边界效应，并且它可以在几乎所有基于DCF的跟踪器中使用。 OTB基准测试的实验表明，与基线跟踪器相比，该框架提高了性能。

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