Neighbourhood-guided Feature Reconstruction for Occluded Person Re-Identification

• URL: http://arxiv.org/abs/2105.07345v1
• Date: Sun, 16 May 2021 03:53:55 GMT
• Title: Neighbourhood-guided Feature Reconstruction for Occluded Person Re-Identification
• Authors: Shijie Yu and Dapeng Chen and Rui Zhao and Haobin Chen and Yu Qiao
• Abstract summary: We propose to reconstruct the feature representation of occluded parts by fully exploiting the information of its neighborhood in a gallery image set. In the large-scale Occluded-DukeMTMC benchmark, our approach achieves 64.2% mAP and 67.6% rank-1 accuracy.
• Score: 45.704612531562404
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Person images captured by surveillance cameras are often occluded by various obstacles, which lead to defective feature representation and harm person re-identification (Re-ID) performance. To tackle this challenge, we propose to reconstruct the feature representation of occluded parts by fully exploiting the information of its neighborhood in a gallery image set. Specifically, we first introduce a visible part-based feature by body mask for each person image. Then we identify its neighboring samples using the visible features and reconstruct the representation of the full body by an outlier-removable graph neural network with all the neighboring samples as input. Extensive experiments show that the proposed approach obtains significant improvements. In the large-scale Occluded-DukeMTMC benchmark, our approach achieves 64.2% mAP and 67.6% rank-1 accuracy which outperforms the state-of-the-art approaches by large margins, i.e.,20.4% and 12.5%, respectively, indicating the effectiveness of our method on occluded Re-ID problem.

Related papers

• Exploring Stronger Transformer Representation Learning for Occluded Person Re-Identification [2.552131151698595]
  We proposed a novel self-supervision and supervision combining transformer-based person re-identification framework, namely SSSC-TransReID. We designed a self-supervised contrastive learning branch, which can enhance the feature representation for person re-identification without negative samples or additional pre-training. Our proposed model obtains superior Re-ID performance consistently and outperforms the state-of-the-art ReID methods by large margins on the mean average accuracy (mAP) and Rank-1 accuracy.
  arXiv  Detail & Related papers  (2024-10-21T03:17:25Z)
• AiOS: All-in-One-Stage Expressive Human Pose and Shape Estimation [55.179287851188036]
  We introduce a novel all-in-one-stage framework, AiOS, for expressive human pose and shape recovery without an additional human detection step. We first employ a human token to probe a human location in the image and encode global features for each instance. Then, we introduce a joint-related token to probe the human joint in the image and encoder a fine-grained local feature.
  arXiv  Detail & Related papers  (2024-03-26T17:59:23Z)
• Learning Feature Recovery Transformer for Occluded Person Re-identification [71.18476220969647]
  We propose a new approach called Feature Recovery Transformer (FRT) to address the two challenges simultaneously. To reduce the interference of the noise during feature matching, we mainly focus on visible regions that appear in both images and develop a visibility graph to calculate the similarity. In terms of the second challenge, based on the developed graph similarity, for each query image, we propose a recovery transformer that exploits the feature sets of its $k$-nearest neighbors in the gallery to recover the complete features.
  arXiv  Detail & Related papers  (2023-01-05T02:36:16Z)
• Occluded Person Re-Identification via Relational Adaptive Feature Correction Learning [8.015703163954639]
  Occluded person re-identification (Re-ID) in images captured by multiple cameras is challenging because the target person is occluded by pedestrians or objects. Most existing methods utilize the off-the-shelf pose or parsing networks as pseudo labels, which are prone to error. We propose a novel Occlusion Correction Network (OCNet) that corrects features through relational-weight learning and obtains diverse and representative features without using external networks.
  arXiv  Detail & Related papers  (2022-12-09T07:48:47Z)
• Dynamic Feature Pruning and Consolidation for Occluded Person Re-Identification [21.006680330530852]
  We propose a feature pruning and consolidation (FPC) framework to circumvent explicit human structure parsing. The framework mainly consists of a sparse encoder, a multi-view feature mathcing module, and a feature consolidation decoder. Our method outperforms state-of-the-art results by at least 8.6% mAP and 6.0% Rank-1 accuracy on the challenging Occluded-Duke dataset.
  arXiv  Detail & Related papers  (2022-11-27T06:18:40Z)
• Dynamic Prototype Mask for Occluded Person Re-Identification [88.7782299372656]
  Existing methods mainly address this issue by employing body clues provided by an extra network to distinguish the visible part. We propose a novel Dynamic Prototype Mask (DPM) based on two self-evident prior knowledge. Under this condition, the occluded representation could be well aligned in a selected subspace spontaneously.
  arXiv  Detail & Related papers  (2022-07-19T03:31:13Z)
• CGUA: Context-Guided and Unpaired-Assisted Weakly Supervised Person Search [54.106662998673514]
  We introduce a Context-Guided and Unpaired-Assisted (CGUA) weakly supervised person search framework. Specifically, we propose a novel Context-Guided Cluster (CGC) algorithm to leverage context information in the clustering process. Our method achieves comparable or better performance to the state-of-the-art supervised methods by leveraging more diverse unlabeled data.
  arXiv  Detail & Related papers  (2022-03-27T13:57:30Z)
• On Improving the Generalization of Face Recognition in the Presence of Occlusions [13.299431908881425]
  Occlusion-aware face REcOgnition (OREO) approach learned discriminative facial templates despite the presence of such occlusions. OREO improved the generalization ability of face recognition under occlusions by (10.17%) in a single-image-based setting.
  arXiv  Detail & Related papers  (2020-06-11T20:17:23Z)
• Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond [59.149653740463435]
  Person re-identification (re-ID) aims at matching images of the same person across camera views. Due to varying distances between cameras and persons of interest, resolution mismatch can be expected. We propose a novel generative adversarial network to address cross-resolution person re-ID.
  arXiv  Detail & Related papers  (2020-02-19T07:21:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.