基于时空加权多实例学习的鲁棒跟踪

Li Wang, Xiao'an Tang, Dongdong Li
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引用次数: 2

摘要

由于在处理标签歧义方面的优势,多实例学习(MIL)被引入到自适应检测跟踪方法中,以减轻漂移,并获得良好的跟踪性能。然而,MIL跟踪器假设正袋中的所有样本对袋概率的贡献相同,这忽略了样本的重要性。为了解决这一问题,本文提出了一种时空加权MIL (STWMIL)跟踪器,该跟踪器将时间权重集成到haar类特征的更新方案中,并将空间权重集成到袋概率函数中。靠近目标位置的阳性样本的空间权重大于远离目标位置的阳性样本,说明前者对阳性袋概率的贡献更大。在空间权重的基础上,利用加权的noise - or模型提出了一种新的袋概率函数。最近获取的图像的时间权重比早期观测值大,这意味着在旧观测值上花费的建模功率更小。基于时间权值,提出了一种学习率变化但收敛的更新方案,并进行了严格的数学证明。在OTB-2013跟踪基准上进行的大量实验表明,我们提出的跟踪器在定性和定量上都优于几种最先进的跟踪器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust Tracking via Spatio-Temporally Weighted Multiple Instance Learning
Due to the superiority in handling label ambiguity, multiple instance learning (MIL) has been introduced into adaptive tracking-by-detection methods to alleviate drift and yields promising tracking performance. However, the MIL tracker assumes that all samples in a positive bag contribute equally to the bag probability, which ignores sample importance. To address this issue, in this paper we propose a spatio- temporally weighted MIL (STWMIL) tracker which integrates temporal weight into the update scheme for Haar-like features and spatial weight into the bag probability function. Spatial weight for the positive sample near the target location is larger than that far from the target location, which means the former contributes more to the positive bag probability. Based on spatial weight, a novel bag probability function is proposed using the weighted Noisy-OR model. Temporal weight for the recently-acquired images is larger than that for the earlier observations, which means less modeling power is expended on old observations. Based on temporal weight, a novel update scheme with changing but convergent learning rate is derived with strict mathematic proof. Extensive experiments performed on the OTB-2013 tracking benchmark demonstrate that our proposed tracker achieves superior performance both qualitatively and quantitatively over several state-of- the-art trackers.
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