Abstract
While gait recognition has seen many advances in recent years, the occlusion problem has largely been ignored. This problem is especially important for gait recognition from uncontrolled outdoor sequences at range - since any small obstruction can affect the recognition system. Most current methods assume the availability of complete body information while extracting the gait features. When parts of the body are occluded, these methods may hallucinate and output a corrupted gait signature as they try to look for body parts which are not present in the input at all. To address this, we exploit the learned occlusion type while extracting identity features from videos. Thus, in this work, we propose an occlusion aware gait recognition method which can be used to model intrinsic occlusion awareness into potentially any state-of-the-art gait recognition method. Our experiments on the challenging GREW and BRIAR datasets show that networks enhanced with this occlusion awareness perform better at recognition tasks than their counterparts trained on similar occlusions.
Abstract (translated)
虽然最近几年在步态识别方面取得了许多进展，但遮挡问题却被大大忽视了。对于未受控的户外序列中的步态识别，这个问题尤为重要，因为任何小的遮挡都可能影响识别系统。大多数现有方法在提取步态特征时假定具有完整的身体信息。当身体部分被遮挡时，这些方法可能会出现幻觉，并输出一个污染的步态签名，因为他们试图寻找在输入中根本不存在的身体部位。为了解决这个问题，我们利用学习到的遮挡类型来提取身份特征。因此，在本文中，我们提出了一个具有遮挡意识的步态识别方法，可以用于将内在遮挡意识建模为可能实现任何最先进的步态识别方法的状态。我们对具有挑战性的GREW和BRIAR数据集的实验结果表明，与训练在类似遮挡条件下的网络相比，具有遮挡意识的网络在识别任务上表现更好。
URL
https://arxiv.org/abs/2312.02290
