A deep multi-feature distance metric learning method for pedestrian re-identification

Deng, Xuan; Liao, Kaiyang; Yao, Zheng; Lin, Guanghui; Lian, Hao

doi:10.1007/s11042-020-10458-8

Cited by 9 publications

(7 citation statements)

References 43 publications

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“…The system incorporates both automatic and semi-automatic identification technologies, with automatic identification being the primary focus. The study explores the link between human brain structure and cognitive processes, revealing that brain structure directly impacts visual cognitive processes [21]. To begin with, our study delves into the developmental process of human visual cognition, revealing that the human visual system's initial ability is to discriminate color areas.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Computer Image Recognition with Improved Image Algorithms

Huang,

Yao,

Zhang

et al. 2024

Preprint

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Advances in computer image recognition have significantly impacted many industries, including healthcare, security and autonomous systems. This paper aims to explore the potential of improving image algorithms to enhance computer image recognition. Specifically, we will focus on regression methods as a means to improve the accuracy and efficiency of identifying images. In this study, we will analyze various regression techniques and their applications in computer image recognition, as well as the resulting performance improvements through detailed examples and data analysis. This paper deals with the problems related to visual image processing in outdoor unstructured environment. Finally, the heterogeneous patterns are converted into the same pattern, and the heterogeneous patterns are extracted from the fusion features of data modes. The simulation results show that the perception ability and recognition ability of outdoor image recognition in complex environment are improved.

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Section: Related Workmentioning

confidence: 99%

Enhancing Computer Image Recognition with Improved Image Algorithms

Huang,

Yao,

Zhang

et al. 2024

Preprint

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“…e update rule of deep learning is shown in (15), in which, the learning rate is used to control the speed of gradient descent. e above is the process of updating parameters by gradient descent method.…”

Section: Deep Learning Back-propagation-related Algorithmmentioning

confidence: 99%

“…irdly, a distance education algorithm based on image discriminant analysis is proposed. Finally, a fusion method is studied, and the artificial cooperation task with depth resolution is completed [15].…”

Section: Introductionmentioning

confidence: 99%

Application of Multi-Feature Fusion Based on Deep Learning in Pedestrian Re-Recognition Method

Han

Zhang

Xie

et al. 2022

Mobile Information Systems

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A system known as pedestrian recognition makes use of several cameras to identify the surrounding area and quickly identify and match the target demographic. Based on pedestrian recognition, the picture model, pedestrian features, and other information, the features are developed to have a high degree of generalizability, distinctiveness, and accuracy. e application approach for pedestrian re-recognition based on deep learning for numerous features is proposed in this paper. e suggested approach successfully preserves high-level semantic information, which helps network members extract all of the pedestrian properties. As external material and semantic information were combined horizontally and vertically, environmental interference was decreased, and people's ability to create networks was enhanced. e voice channel of the speech system was introduced in order to fully utilize the global information network, and the connection between the channels was carefully addressed in order to enhance the global information network's capacity for expression. e null convolution reduced the operational continuity of the identication information. To increase the consistency of the data, the multi-level spatial convolution structure was merged with the entire image in this paper. After numerous experiments, the three groups were 89.5%, 89.5%, and 89.1%, respectively, compared to 1501, DukeMTMC-reID, CUHK03, and other medial groups, and the experimental results were 85% and 89.5%, respectively. e multimode feedback MP3 module was taken from the MP3 module in order to gain richer and denser multimode feature information. Comparing the module's initial response level (RANK1) with the various cycles yields the average accuracy for each cycle (catalog). e experiment demonstrates that the two mixed pile groups can enhance the modulus of the mixed pile group and get better results. e multi-level multi-scale pole function e ectively combines the characteristics of pedestrians in various scales, and the addition of the ASP module enhanced the network context information's overall ability to be represented, aided in this chapter's research method's ability to more thoroughly analyze scene structure, and increased the precision of pedestrian rerecognition.

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“…Traditional research on person re-id mainly includes visual feature representation [18,19] and distance metric learning [20,21]. In [22], feature effectiveness was identified in a query-adaptive manner for feature fusion.…”

Section: Person Re-identificationmentioning

confidence: 99%

Online Discrete Anchor Graph Hashing for Mobile Person Re-Identification

Xie

Fang

2021

Journal of Advanced Transportation

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With the advance of mobile technologies, mobile devices such as unmanned aerial vehicle (UAV) become more important in video surveillance. By applying mobile person re-identification (re-id), mobile devices can monitor pedestrians in the transportation system from complex environments. Since the computing and storage resources of mobile devices are limited, traditional person re-id methods are not appropriate for mobile condition. Besides, mobile person re-id task also requires real-time processing. In this paper, we propose a novel hashing method: online discrete anchor graph hashing (ODAGH) for mobile person re-id. ODAGH integrates the advantages of online learning and hashing technology. In ODAGH, we propose an online discrete optimization algorithm to improve the efficiency of anchor graph learning in the online scenario. Experimental results demonstrate the superiority of ODAGH in terms of both effect and efficiency.

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A deep multi-feature distance metric learning method for pedestrian re-identification

Cited by 9 publications

References 43 publications

Enhancing Computer Image Recognition with Improved Image Algorithms

Enhancing Computer Image Recognition with Improved Image Algorithms

Application of Multi-Feature Fusion Based on Deep Learning in Pedestrian Re-Recognition Method

Online Discrete Anchor Graph Hashing for Mobile Person Re-Identification

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