A Weighted Center Graph Fusion Method for Person Re-Identification

Geng, Shuze; Yu, Ming; Guo, Yingchun; Yu, Yang

doi:10.1109/access.2019.2898729

Cited by 3 publications

(1 citation statement)

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“…Most of the previously published works directly learn the feature representations from the whole pedestrian image, that contains background clutter. Quite recently, several person ReID deep learning-based systems have suggested learning effective feature representations from the detected pedestrian body to reduce the background clutter and improve the robustness of the person ReID system [6][7][8]. This motivates us to develop an automated image segmentation algorithm to eliminate background noise interference issues and enhance the discriminability of the extracted feature representations, even for an incomplete person, which may contain information that is discriminatory and deserves attention.…”

Section: Introductionmentioning

confidence: 99%

ReID-DeePNet: A Hybrid Deep Learning System for Person Re-Identification

Mohammed

Al-Fahdawi

Al-Waisy³

et al. 2022

Mathematics

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Person re-identification has become an essential application within computer vision due to its ability to match the same person over non-overlapping cameras. However, it is a challenging task because of the broad view of cameras with a large number of pedestrians appearing with various poses. As a result, various approaches of supervised model learning have been utilized to locate and identify a person based on the given input. Nevertheless, several of these approaches perform worse than expected in retrieving the right person in real-time over multiple CCTVs/camera views. This is due to inaccurate segmentation of the person, leading to incorrect classification. This paper proposes an efficient and real-time person re-identification system, named ReID-DeePNet system. It is based on fusing the matching scores generated by two different deep learning models, convolutional neural network and deep belief network, to extract discriminative feature representations from the pedestrian image. Initially, a segmentation procedure was developed based on merging the advantages of the Mask R-CNN and GrabCut algorithm to tackle the adverse effects caused by background clutter. Afterward, the two different deep learning models extracted discriminative feature representations from the pedestrian segmented image, and their matching scores were fused to make the final decision. Several extensive experiments were conducted, using three large-scale and challenging person re-identification datasets: Market-1501, CUHK03, and P-DESTRE. The ReID-DeePNet system achieved new state-of-the-art Rank-1 and mAP values on these three challenging ReID datasets.

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Section: Introductionmentioning

confidence: 99%

ReID-DeePNet: A Hybrid Deep Learning System for Person Re-Identification

Mohammed

Al-Fahdawi

Al-Waisy³

et al. 2022

Mathematics

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Person Re-Identification using Background Subtraction and Siamese Network for Pose Varians

Nabila,

Wahyono

2022

2022 8th International Conference on Science and Technology (ICST)

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Distributed Network of Adaptive and Self-Reconfigurable Active Vision Systems

Shashank

Sreedevi

2022

Symmetry

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The performance of a computer vision system depends on the accuracy of visual information extracted by the sensors and the system’s visual-processing capabilities. To derive optimum information from the sensed data, the system must be capable of identifying objects of interest (OOIs) and activities in the scene. Active vision systems intend to capture OOIs with the highest possible resolution to extract the optimum visual information by calibrating the configuration spaces of the cameras. As the data processing and reconfiguration of cameras are interdependent, it becomes very challenging for advanced active vision systems to perform in real time. Due to limited computational resources, model-based asymmetric active vision systems only work in known conditions and fail miserably in unforeseen conditions. Symmetric/asymmetric systems employing artificial intelligence, while they manage to tackle unforeseen environments, require iterative training and thus are not reliable for real-time applications. Thus, the contemporary symmetric/asymmetric reconfiguration systems proposed to obtain optimum configuration spaces of sensors for accurate activity tracking and scene understanding may not be adequate to tackle unforeseen conditions in real time. To address this problem, this article presents an adaptive self-reconfiguration (ASR) framework for active vision systems operating co-operatively in a distributed blockchain network. The ASR framework enables active vision systems to share their derived learning about an activity or an unforeseen environment, which learning can be utilized by other active vision systems in the network, thus lowering the time needed for learning and adaptation to new conditions. Further, as the learning duration is reduced, the duration of the reconfiguration of the cameras is also reduced, yielding better performance in terms of understanding of a scene. The ASR framework enables resource and data sharing in a distributed network of active vision systems and outperforms state-of-the-art active vision systems in terms of accuracy and latency, making it ideal for real-time applications.

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A Weighted Center Graph Fusion Method for Person Re-Identification

Cited by 3 publications

References 60 publications

ReID-DeePNet: A Hybrid Deep Learning System for Person Re-Identification

ReID-DeePNet: A Hybrid Deep Learning System for Person Re-Identification

Person Re-Identification using Background Subtraction and Siamese Network for Pose Varians

Distributed Network of Adaptive and Self-Reconfigurable Active Vision Systems

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