Illumination-Adaptive Person Re-Identification

Zeng, Zelong; Wang, Zhixiang; Wang, Zheng; Zheng, Yinqiang; Chuang, Yung-Yu; Satoh, Shin’ichi

doi:10.1109/tmm.2020.2969782

Cited by 79 publications

(21 citation statements)

References 43 publications

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“…Many researchers have focused on introducing novel architectures to address the problem [9,[14][15][16][17][18][19][20][21]. In this section, we briefly discuss some recent deep architectures.…”

Section: Related Workmentioning

confidence: 99%

Transformers in Pedestrian Image Retrieval and Person Re-Identification in a Multi-Camera Surveillance System

Tahir

Anwar

2021

Applied Sciences

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Person Re-Identification is an essential task in computer vision, particularly in surveillance applications. The aim is to identify a person based on an input image from surveillance photographs in various scenarios. Most Person re-ID techniques utilize Convolutional Neural Networks (CNNs); however, Vision Transformers are replacing pure CNNs for various computer vision tasks such as object recognition, classification, etc. The vision transformers contain information about local regions of the image. The current techniques take this advantage to improve the accuracy of the tasks underhand. We propose to use the vision transformers in conjunction with vanilla CNN models to investigate the true strength of transformers in person re-identification. We employ three backbones with different combinations of vision transformers on two benchmark datasets. The overall performance of the backbones increased, showing the importance of vision transformers. We provide ablation studies and show the importance of various components of the vision transformers in re-identification tasks. .

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Section: Related Workmentioning

confidence: 99%

Transformers in Pedestrian Image Retrieval and Person Re-Identification in a Multi-Camera Surveillance System

Tahir

Anwar

2021

Applied Sciences

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“…Appearance based —Identifying people from their silhouettes can be approached as a re-identification (ReID) problem [ 18 , 19 , 20 ]. The vast majority of the literature on person ReID makes use of RGB images, as detailed in the review from Bedagkar-Gala et al [ 21 ] and the more recent deep-learning review from Wu et al [ 22 ].…”

Section: Related Workmentioning

confidence: 99%

Person Re-ID by Fusion of Video Silhouettes and Wearable Signals for Home Monitoring Applications

Masullo

Burghardt

Damen

et al. 2020

Sensors

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The use of visual sensors for monitoring people in their living environments is critical in processing more accurate health measurements, but their use is undermined by the issue of privacy. Silhouettes, generated from RGB video, can help towards alleviating the issue of privacy to some considerable degree. However, the use of silhouettes would make it rather complex to discriminate between different subjects, preventing a subject-tailored analysis of the data within a free-living, multi-occupancy home. This limitation can be overcome with a strategic fusion of sensors that involves wearable accelerometer devices, which can be used in conjunction with the silhouette video data, to match video clips to a specific patient being monitored. The proposed method simultaneously solves the problem of Person ReID using silhouettes and enables home monitoring systems to employ sensor fusion techniques for data analysis. We develop a multimodal deep-learning detection framework that maps short video clips and accelerations into a latent space where the Euclidean distance can be measured to match video and acceleration streams. We train our method on the SPHERE Calorie Dataset, for which we show an average area under the ROC curve of 76.3% and an assignment accuracy of 77.4%. In addition, we propose a novel triplet loss for which we demonstrate improving performances and convergence speed.

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“…Inspired by the tremendous success of deep learning, many methods [4], [5], [6] have been introduced to learn deep expressive representations for person ReID and achieved stateof-the-art performance. Typically, most of these methods [7], [8], [4], [9], [5], [10], [11], [12], [6], [13], [14], [15], [16], [17], [18], [19], [20] employ a triplet loss [7], [5], [13] or its combination of a classification loss [10], [11], [12] as the driving force to extract relevant features. Under this generic framework, several approaches have been developed to learn semantically-rich and/or local features, such as the global feature-based approach [14], [15], data augmentation-based approach [6], [13] and striping approach [21], [10].…”

Section: Introductionmentioning

confidence: 99%

Beyond Triplet Loss: Person Re-Identification With Fine-Grained Difference-Aware Pairwise Loss

Cheng

Pang

Bai

et al. 2022

IEEE Trans. Multimedia

167

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Person Re-IDentification (ReID) aims at reidentifying persons from different viewpoints across multiple cameras. Capturing the fine-grained appearance differences is often the key to accurate person ReID, because many identities can be differentiated only when looking into these finegrained differences. However, most state-of-the-art person ReID approaches, typically driven by a triplet loss, fail to effectively learn the fine-grained features as they are focused more on differentiating large appearance differences. To address this issue, we introduce a novel pairwise loss function that enables ReID models to learn the fine-grained features by adaptively enforcing an exponential penalization on the images of small differences and a bounded penalization on the images of large differences. The proposed loss is generic and can be used as a plugin to replace the triplet loss to significantly enhance different types of stateof-the-art approaches. Experimental results on four benchmark datasets show that the proposed loss substantially outperforms a number of popular loss functions by large margins; and it also enables significantly improved data efficiency.

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Illumination-Adaptive Person Re-Identification

Cited by 79 publications

References 43 publications

Transformers in Pedestrian Image Retrieval and Person Re-Identification in a Multi-Camera Surveillance System

Transformers in Pedestrian Image Retrieval and Person Re-Identification in a Multi-Camera Surveillance System

Person Re-ID by Fusion of Video Silhouettes and Wearable Signals for Home Monitoring Applications

Beyond Triplet Loss: Person Re-Identification With Fine-Grained Difference-Aware Pairwise Loss

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