Re-ID Driven Localization Refinement for Person Search

Han, Chuchu; Ye, Jiacheng; Zhong, Yong; Tan, Xin; Zhang, Chi; Gao, Changxin; Sang, Nong

doi:10.1109/iccv.2019.00991

Cited by 129 publications

(81 citation statements)

References 34 publications

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“…The task-cascaded methods adopt two separate steps of detection and re-ID. In this group, MGTS [3] uses VGGNet [27] based Faster R-CNN [25] detector, CLSA [16] employs vanilla Faster R-CNN, while RDLR [12] uses Faster R-CNN with FPN [19]. Evaluation on PRW.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

“…These methods use separate pre-trained pedestrian detectors and only learn the re-ID networks, thus are not end-toend trainable. Recently, Hanet al [12] propose to refine localization using re-ID training loss, in which way person-to-person clutter can be reduced while accessory could be appropriately encompassed for re-ID feature extraction.…”

Section: Related Workmentioning

confidence: 99%

“…The pedestrian detection branch performs bounding boxes binary classification and regression, which are then used to pool Region of Interest (RoI) features from the backbone (a.k.a RoI Pooling [9] in detection) and send it to a separate re-ID branch for identification features extraction. The task-cascaded methods [3,12,16,40] formulate it as a cascaded framework. In the pipeline, a pedestrian detector first takes a full-size video frame as input and produces bounding boxes of pedestrians.…”

Section: Introductionmentioning

confidence: 99%

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Bottom-Up Foreground-Aware Feature Fusion for Person Search

Yang

Chen

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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The key to efficient person search is jointly localizing pedestrians and learning discriminative representation for person re-identification (re-ID). Some recently developed task-joint models are built with separate detection and re-ID branches on top of shared region feature extraction networks, where the large receptive field of neurons leads to background information redundancy for the following re-ID task. Our diagnostic analysis indicates the task-joint model suffers from considerable performance drop when the background is replaced or removed. In this work, we propose a subnet to fuse the bounding box features that pooled from multiple ConvNet stages in a bottom-up manner, termed bottom-up fusion (BUF) network. With a few parameters introduced, BUF leverages the multi-level features with different sizes of receptive fields to mitigate the backgroundbias problem. Moreover, the newly introduced segmentation head generates a foreground probability map as guidance for the network to focus on the foreground regions. The resulting foreground attention module (FAM) enhances the foreground features. Extensive experiments on PRW and CUHK-SYSU validate the effectiveness of the proposals. Our Bottom-Up Foreground-Aware Feature Fusion (BUFF) network achieves considerable gains over the state-of-thearts on PRW and competitive performance on CUHK-SYSU.

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Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bottom-Up Foreground-Aware Feature Fusion for Person Search

Yang

Chen

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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“…Some two-step methods [1,2,3] improved the feature extraction and metric learning of the re-identification model. Some two-step methods [4,5,6] had improvements on the correlation between the detection model and the re-identification model.…”

Section: Introductionmentioning

confidence: 99%

Stable and Effective One-Step Method for Person Search

Xiang

Wang

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Person search, which requires both pedestrian detection and person re-identification, is a challenging computer vision task applied to real-world scenarios. The challenges faced by detection and re-identification, such as occlusion, poor illumination, confusing background, are still urgent for person search. In addition, one-step methods for person search need to deal with the divergence between two tasks. In this work, we propose an end-to-end model containing the feature extractor, the region proposal network, and the multi-task learning module. In order to process divergence between detection and re-identification, we introduce switchable normalization and gradient centralization to improve the stability of the model. To solve the imbalance problem of hard examples, we introduce focal loss as a classification loss in the multi-task learning module. The experimental results on two benchmarks, i.e., CUHK-SYSU and PRW, well demonstrate that our method outperforms the state-of-the-art one-step methods.

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“…由于训练数据与测试数据的行人身份不重合, 所以也看做一种零样本学习 [1] 问题. 该技术可以与行人检测 [5] 、目标跟踪 [2] 等技术结合应用于智能视频监控、智能安保、智能交通等领域. 在现实场景中, 如刑侦破案以及特定场景下的识人、寻人问题上具有广泛的应用前景.…”

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