Proceedings ArticleDOI
See the Forest for the Trees: Joint Spatial and Temporal Recurrent Neural Networks for Video-Based Person Re-identification
Zhen Zhou,Yan Huang,Wei Wang,Liang Wang,Tieniu Tan +4 more
- pp 6776-6785
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TLDR
This paper focuses on video-based person re-identification and builds an end-to-end deep neural network architecture to jointly learn features and metrics and integrates the surrounding information at each location by a spatial recurrent model when measuring the similarity with another pedestrian video.Abstract:Â
Surveillance cameras have been widely used in different scenes. Accordingly, a demanding need is to recognize a person under different cameras, which is called person re-identification. This topic has gained increasing interests in computer vision recently. However, less attention has been paid to video-based approaches, compared with image-based ones. Two steps are usually involved in previous approaches, namely feature learning and metric learning. But most of the existing approaches only focus on either feature learning or metric learning. Meanwhile, many of them do not take full use of the temporal and spatial information. In this paper, we concentrate on video-based person re-identification and build an end-to-end deep neural network architecture to jointly learn features and metrics. The proposed method can automatically pick out the most discriminative frames in a given video by a temporal attention model. Moreover, it integrates the surrounding information at each location by a spatial recurrent model when measuring the similarity with another pedestrian video. That is, our method handles spatial and temporal information simultaneously in a unified manner. The carefully designed experiments on three public datasets show the effectiveness of each component of the proposed deep network, performing better in comparison with the state-of-the-art methods.read more
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Posted Content
Deep Learning for Person Re-identification: A Survey and Outlook
TL;DR: A powerful AGW baseline is designed, achieving state-of-the-art or at least comparable performance on twelve datasets for four different Re-ID tasks, and a new evaluation metric (mINP) is introduced, indicating the cost for finding all the correct matches, which provides an additional criteria to evaluate the Re- ID system for real applications.
Proceedings ArticleDOI
Mask-Guided Contrastive Attention Model for Person Re-identification
TL;DR: This paper introduces the binary segmentation masks to construct synthetic RGB-Mask pairs as inputs, then designs a mask-guided contrastive attention model (MGCAM) to learn features separately from the body and background regions, and proposes a novel region-level triplet loss to restrain the features learnt from different regions.
Proceedings ArticleDOI
A Pose-Sensitive Embedding for Person Re-identification with Expanded Cross Neighborhood Re-ranking
TL;DR: In this paper, the fine and coarse pose information of the person was incorporated into CNN to learn a discriminative embedding and achieved state-of-the-art performance on a number of challenging surveillance image and video datasets.
Posted Content
AlignedReID: Surpassing Human-Level Performance in Person Re-Identification.
TL;DR: This paper proposes a novel method called AlignedReID that extracts a global feature which is jointly learned with local features, and is the first to surpass human-level performance on Market1501 and CUHK03, two widely used Person ReID datasets.
Book ChapterDOI
Part-Aligned Bilinear Representations for Person Re-Identification
TL;DR: A novel network that learns a part-aligned representation for person re-identification that handles the body part misalignment problem, that is, body parts are misaligned across human detections due to pose/viewpoint change and unreliable detection.
References
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