Unsupervised Learning Boost Person Re-identification and Real World Application

Lin, Yang‐Sheng; Yan, Kai; Du, Xiangcheng; Lin, Yining; Peng, Yao

doi:10.1109/bigdata52589.2021.9671979

“…The mentioned structures upgraded the core feature extractor from YOLOv3 to YOLOV5 as the backbone, which verified that the replacement of the backbone and feature extractors could increase the recognition accuracy compared to the previous version. The research [131] also utilized YOLOv5 for recognizing the human key points and combined it with swin transformer, which used the multi-head mechanism on top of large-scale unsupervised data.…”

Section: A Yolo Series On Human Identificationmentioning

confidence: 99%

Deep Neural Networks in Video Human Action Recognition: A review

Wang¹,

Zheng²,

yang³

et al. 2023

Preprint

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<p>Currently, spatial-temporal behavior recognition is one of the most foundational tasks of computer vision. The 2D neural networks of deep learning are built for recognizing pixel-level information such as images with RGB, RGB-D, or optical flow formats, with the current increasingly wide usage of surveillance video and more tasks related to human action recognition. There are increasing tasks requiring temporal information for frames dependency analysis. The researchers have widely studied video-based recognition rather than image-based(pixel-based) only to extract more informative elements from geometry tasks. Our current related research addresses multiple novels proposed research works and compares their advantages and disadvantages between the derived deep learning frameworks rather than machine learning frameworks. The comparison happened between existing frameworks and datasets, which are video format data only. Due to the specific properties of human actions and the increasingly wide usage of deep neural networks, we collected all research works within the last three years between 2020 to 2022. In our article, the performance of deep neural networks surpassed most of the techniques in the feature learning and extraction tasks, especially video action recognition.</p>

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“…The mentioned structures upgraded the core feature extractor from YOLOv3 to YOLOV5 as the backbone, which verified that the replacement of the backbone and feature extractors could increase the recognition accuracy compared to the previous version. The research [131] also utilized YOLOv5 for recognizing the human key points and combined it with swin transformer, which used the multi-head mechanism on top of large-scale unsupervised data.…”

Section: A Yolo Series On Human Identificationmentioning

confidence: 99%

Deep Neural Networks in Video Human Action Recognition: A review

Wang¹,

Zheng²,

yang³

et al. 2023

Preprint

2

0

View full text Add to dashboard Cite

<p>Currently, spatial-temporal behavior recognition is one of the most foundational tasks of computer vision. The 2D neural networks of deep learning are built for recognizing pixel-level information such as images with RGB, RGB-D, or optical flow formats, with the current increasingly wide usage of surveillance video and more tasks related to human action recognition. There are increasing tasks requiring temporal information for frames dependency analysis. The researchers have widely studied video-based recognition rather than image-based(pixel-based) only to extract more informative elements from geometry tasks. Our current related research addresses multiple novels proposed research works and compares their advantages and disadvantages between the derived deep learning frameworks rather than machine learning frameworks. The comparison happened between existing frameworks and datasets, which are video format data only. Due to the specific properties of human actions and the increasingly wide usage of deep neural networks, we collected all research works within the last three years between 2020 to 2022. In our article, the performance of deep neural networks surpassed most of the techniques in the feature learning and extraction tasks, especially video action recognition.</p>

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An enhanced Swin Transformer for soccer player reidentification

Akan,

Varlı,

Bhuiyan

2024

Sci Rep

0

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The re-identification (ReID) of objects in images is a widely studied topic in computer vision, with significant relevance to various applications. The ReID of players in broadcast videos of team sports is the focus of this study. We specifically focus on identifying the same player in images taken at any given moment during a game from various camera angles. This work varies from other person ReID apps since the same team wears very similar clothes, there are few samples for each identification, and image resolutions are low. One of the hardest parts of object ReID is robust feature representation extraction. Despite the great success of current convolutional neural network-based (CNN) methods, most studies only consider learning representations from images, neglecting long-range dependency. Transformer-based model studies are increasing and yielding encouraging results. Transformers still have trouble extracting features from small objects and visual cues. To address these issues, we enhanced the Swin Transformer with the levering of CNNs. We created a regional feature extraction Swin Transformer (RFES) backbone to increase local feature extraction and small-scale object feature extraction. We also use three loss functions to handle imbalanced data and highlight challenging situations. Re-ranking with k-reciprocal encoding was used in this study's retrieval phase, and its assessment findings were provided. Finally, we conducted experiments on the Market-1501 and SoccerNet-v3 ReID datasets. Experimental results show that the proposed re-ID method reaches rank-1 accuracy of 96.2% with mAP: 89.1 and rank-1 accuracy of 84.1% with mAP: 86.7 on the Market-1501 and SoccerNet-v3 datasets, respectively, outperforming the state-of-the-art approaches.

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Unsupervised Learning Boost Person Re-identification and Real World Application

Cited by 3 publications

References 22 publications

Deep Neural Networks in Video Human Action Recognition: A review

Deep Neural Networks in Video Human Action Recognition: A review

Deep Neural Networks in Video Human Action Recognition: A review

An enhanced Swin Transformer for soccer player reidentification

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