Existing gait recognition frameworks retrieve an identity in the gallery based on the distance between a probe sample and the identities in the gallery. However, existing methods often neglect that the gallery may not contain identities corresponding to the probes, leading to recognition errors rather than raising an alarm. In this paper, we introduce a novel uncertainty-aware gait recognition method that models the uncertainty of identification based on learned evidence. Specifically, we treat our recognition model as an evidence collector to gather evidence from input samples and parameterize a Dirichlet distribution over the evidence. The Dirichlet distribution essentially represents the density of the probability assigned to the input samples. We utilize the distribution to evaluate the resultant uncertainty of each probe sample and then determine whether a probe has a counterpart in the gallery or not. To the best of our knowledge, our method is the first attempt to tackle gait recognition with uncertainty modelling. Moreover, our uncertain modeling significantly improves the robustness against out-of-distribution (OOD) queries. Extensive experiments demonstrate that our method achieves state-of-the-art performance on datasets with OOD queries, and can also generalize well to other identity-retrieval tasks. Importantly, our method outperforms the state-of-the-art by a large margin of 44.19% when the OOD query rate is around 50% on OUMVLP.
翻译:根据探测样品与画廊中身份之间的距离,现有识别行距框架在画廊中检索到一个身份。然而,现有方法往往忽视画廊可能不包含与探测器对应的身份,从而导致识别错误而不是提醒。在本文中,我们采用了一种新颖的、有不确定觉悟的识别方法,根据所学证据来模拟身份的不确定性。具体地说,我们把我们的识别模型作为证据采集模型,从输入样品中收集证据,并对证据的Drichlet分布进行参数化。Drichlet的分布基本上代表了分配给输入样品的概率的密度。我们利用该分布来评估每个探测器样本中产生的不确定性,然后确定一个探测器是否在画廊中有一个对应方。我们最了解的是,我们的方法是首次尝试用不确定性模型来解决对身份识别的识别。此外,我们不确定的模型极大地提高了从数据流出(OOOD)查询的可靠性。 广泛实验表明,我们的方法在OOD查询中达到了最先进的性性表现,并且还可以将其它身份定位-Prievor-L在OD19大比例上采用的方法。