Oscillation Detection and Parameter-Adaptive Hedge Algorithm for Real-Time Visual Tracking
Oscillation Detection and Parameter-Adaptive Hedge Algorithm for Real-Time Visual Tracking
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DOI：
10.1007/978-3-030-03341-5_20
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
Bolin Lv;Xiaolong Zhou;Shengyong Chen
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文献类型：
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作者：
Bolin Lv;Xiaolong Zhou;Shengyong Chen
尽管基于相关滤波的方法具有很高的视觉跟踪效率，但当存在遮挡时，其跟踪精度可能会大大降低。为了解决这一问题，本文提出了一种新的频谱振荡检测算法和实时跟踪的在线学习策略。首先，为了便于跟踪在线学习自身调整权值，提出了一种加权参数自适应Hedge算法来减少调整参数；其次，由于相干滤波器的频谱在遮挡发生时会发生波动，提出了一种频谱振荡检测算法来检测目标振荡水平的频谱响应。第三，提出了一种回溯算法，在检测到频谱振荡时预测目标位置。最后，引入更新索引来确定当前帧是否被更新，以提高跟踪精度和鲁棒性。在VOT2016和OTB-2015上进行的实验表明，所提出的跟踪方法具有良好的性能，并且与最先进的跟踪方法具有竞争力。
Although correlation filter-based method performs high efficiency for visual tracking, its tracking precision may be greatly degraded when occlusion occurs. To remedy this, this paper proposes a new spectrum oscillation detection algorithm and an online learning strategy for real-time tracking. Firstly, to facilitate the tracking online learning to adjust weights itself, a weighted parameter-adaptive Hedge algorithm is presented to reduce the parameters of the adjustment. Secondly, since the spectrum of the correlation filter will fluctuate when occlusion occurs, a spectrum oscillation detection algorithm is proposed to detect the frequency spectrum response at target oscillation level. Thirdly, a backtracking algorithm is proposed to predict object position when the spectrum oscillation has been detected. Finally, an update index is introduced to determine whether the current frame is updated to improve tracking accuracy and robustness. Experiments conducted on VOT2016 and OTB-2015 demonstrate the good performance of the proposed tracking method and competitive performance against the state-of-the-art tracking methods.