论文标题
不要打扰我：在其他行人的干扰下，人重新识别
Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians
论文作者
论文摘要
在传统的人重新环境中，广泛认为裁剪的人的图像适用于每个人。但是，在一个拥挤的场景中，离岸探测器可能会产生涉及多个人的边界框，其中很大一部分背景行人或人类遮挡存在。从此类裁剪图像中提取的表示，其中包含目标和干扰行人，可能包括分散的信息。这将导致错误的检索结果。为了解决这个问题，本文介绍了一个新颖的深网，称为行人抑制网络（PISNET）。在查询的指导下，PISNET利用查询引导的注意块（QGAB）来增强图库中目标的特征。此外，涉及指导逆转了注意模块和多人分离损失促进了QGAB，以抑制其他行人的干扰。我们的方法对两个新的行人干扰数据集进行了评估，结果表明，所提出的方法对现有的RE-ID方法有利。
In the conventional person Re-ID setting, it is widely assumed that cropped person images are for each individual. However, in a crowded scene, off-shelf-detectors may generate bounding boxes involving multiple people, where the large proportion of background pedestrians or human occlusion exists. The representation extracted from such cropped images, which contain both the target and the interference pedestrians, might include distractive information. This will lead to wrong retrieval results. To address this problem, this paper presents a novel deep network termed Pedestrian-Interference Suppression Network (PISNet). PISNet leverages a Query-Guided Attention Block (QGAB) to enhance the feature of the target in the gallery, under the guidance of the query. Furthermore, the involving Guidance Reversed Attention Module and the Multi-Person Separation Loss promote QGAB to suppress the interference of other pedestrians. Our method is evaluated on two new pedestrian-interference datasets and the results show that the proposed method performs favorably against existing Re-ID methods.
