Improving Person Re-Identification by Combining Siamese Convolutional Neural Network and Re-Ranking Process

Mansouri, Nabila; Ammar, Sourour; Kessentini, Yousri

doi:10.1109/avss.2019.8909902

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…However, the limitation of this method is that it is only effective if there have other rankings (at least three) except the current probe ranking. In addition to the above methods based on content and context information [29], [30], [37], [204], there are many re-ranking methods in person re-identification, such as handcrafted annotation or labeling supervision [31]- [33], common nearest neighbors [34], [122], [207], as well as k-reciprocal method [35], [36], [39], [122], [203].…”

Section: Post Processingmentioning

confidence: 99%

A Comprehensive Overview of Person Re-Identification Approaches

Wang

Zhao

et al. 2020

IEEE Access

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Person re-identification, identifying and tracking pedestrians in cross-domain monitoring systems, is an important technology in the computer vision field and of real significance for the construction of smart cities. With the development of deep learning techniques, especially convolutional neural networks, this technology has received more extensive attention and improvement in recent years and a large number of noteworthy achievements have emerged. This paper provides a comprehensive overview of person re-identification approaches to assist researchers in quickly understand this field with preference as well as to provide a more structured framework. By reviewing more than 300 re-identification related papers, the focus of these studies is summarized as information extraction, metric learning, post-processing, efficiency improvement, labeling cost reduction, and data type expansion. This classification is then organized based on different technologies, and on this basis, the pros and cons of each technology are analyzed. Moreover, this overview summarizes the difficulties and challenges of re-identification and discusses the possible research directions for reference. INDEX TERMSComputer vision, convolutional neural networks, distance learning, feature extraction, person re-identification.

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Section: Post Processingmentioning

confidence: 99%

A Comprehensive Overview of Person Re-Identification Approaches

Wang

Zhao

et al. 2020

IEEE Access

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“…However, the above solutions ignore the challenge of the same pedestrian's background variations in different cameras. Some state-of-the-art methods [10][11][12] have been researched for such problems. For example, Song et al [7] designed a mask-guided contrastive attention model (MGCAM) to learn features separately from the body and background regions.…”

Section: Introductionmentioning

confidence: 99%

Swin Transformer Based on Two-Fold Loss and Background Adaptation Re-Ranking for Person Re-Identification

et al. 2022

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Person re-identification (Re-ID) aims to identify the same pedestrian from a surveillance video in various scenarios. Existing Re-ID models are biased to learn background appearances when there are many background variations in the pedestrian training set. Thus, pedestrians with the same identity will appear with different backgrounds, which interferes with the Re-ID performance. This paper proposes a swin transformer based on two-fold loss (TL-TransNet) to pay more attention to the semantic information of a pedestrian’s body and preserve valuable background information, thereby reducing the interference of corresponding background appearance. TL-TransNet is supervised by two types of losses (i.e., circle loss and instance loss) during the training phase. In the retrieval phase, DeepLabV3+ as a pedestrian background segmentation model is applied to generate body masks in terms of query and gallery set. The background removal results are generated according to the mask and are used to filter out interfering background information. Subsequently, a background adaptation re-ranking is designed to combine the original information with the background-removed information, which digs out more positive samples with large background deviation. Extensive experiments on two public person Re-ID datasets testify that the proposed method achieves competitive robustness performance in terms of the background variation problem.

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Fusing Local and Global Features for Person Re-identification Using Multi-stream Deep Neural Networks

Ghorbel

Ammar

Kessentini

et al. 2021

Pattern Recognition and Artificial Intelligence

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The field of person re-identification remains a challenging topic in video surveillance and public security because it is facing many problems related to the variations of the position, background and brightness scenes. In order to minimize the impact of those variations, we introduce in this work a multi-stream re-identification system based on the fusion of local and global features. The proposed system uses first a body partition segmentation network (SEG-CNN) to segment three different body regions (the whole body part, the middle and the down body parts) that will represent local features. While the original image will be used to extract global features. Second, a multi-stream fusion framework is performed to fuse the outputs of the individual streams and generate the final predictions. We experimentally prove that the multi-stream combination method improves the recognition rates and provides better results than classic fusion methods. In the rank-1/mAP, the improvement is of /9, 5 for the Market-1501 benchmark dataset.

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Improving Person Re-Identification by Combining Siamese Convolutional Neural Network and Re-Ranking Process

Cited by 5 publications

References 18 publications

A Comprehensive Overview of Person Re-Identification Approaches

A Comprehensive Overview of Person Re-Identification Approaches

Swin Transformer Based on Two-Fold Loss and Background Adaptation Re-Ranking for Person Re-Identification

Fusing Local and Global Features for Person Re-identification Using Multi-stream Deep Neural Networks

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