Adaptive Deep Metric Embeddings for Person Re-Identification under Occlusions
This addresses the problem of identifying occluded persons in video surveillance, offering a novel approach with strong specific gains.
The paper tackles person re-identification under occlusions by proposing a method that learns spatial dependencies between local regions using LSTM and an adaptive nearest neighbor loss to reduce intra-class variations and enlarge inter-class differences, resulting in significantly improved performance on challenging datasets compared to state-of-the-art methods.
Person re-identification (ReID) under occlusions is a challenging problem in video surveillance. Most of existing person ReID methods take advantage of local features to deal with occlusions. However, these methods usually independently extract features from the local regions of an image without considering the relationship among different local regions. In this paper, we propose a novel person ReID method, which learns the spatial dependencies between the local regions and extracts the discriminative feature representation of the pedestrian image based on Long Short-Term Memory (LSTM), dealing with the problem of occlusions. In particular, we propose a novel loss (termed the adaptive nearest neighbor loss) based on the classification uncertainty to effectively reduce intra-class variations while enlarging inter-class differences within the adaptive neighborhood of the sample. The proposed loss enables the deep neural network to adaptively learn discriminative metric embeddings, which significantly improve the generalization capability of recognizing unseen person identities. Extensive comparative evaluations on challenging person ReID datasets demonstrate the significantly improved performance of the proposed method compared with several state-of-the-art methods.