True-Color and Grayscale Video Person Re-Identification

Ma, Fei; Jing, Xiao‐Yuan; Zhu, Xiao Ke; Tang, Zhenmin; Peng, Zhiping

doi:10.1109/tifs.2019.2917160

Cited by 19 publications

(8 citation statements)

References 45 publications

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“…Blurring, the most commonly used retouching function, aims to eliminate noise or introduce special effects. (3) Among all image segmentation methods, thresholding is the simplest and most efficient approach for distinguishing a foreground from a background. Specifically, it can be applied to identify the optimal threshold value.…”

Section: Literature Review and Methodologymentioning

confidence: 99%

Camera Handoff for Multicamera Multiobject Tracking

Wang¹,

Sheu²,

Lai³

2022

Sensors and Materials

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In recent years, the unmanned economy has begun to develop rapidly worldwide. Unmanned shops use technology to replace human personnel and increase efficiency. This technology has increased the demand for accurate and efficient camera surveillance systems. Camera handoff is a crucial step in the tasks of continuous target tracking and the maintenance of consistent crosscamera target marking in a multicamera surveillance system. In this study, we proposed a method of indoor multicamera handoff. To ensure continuous target tracking, the minimum number of required frames was maintained and at least one camera tracked each target. We employed the background subtraction approach to detect the target. Next, we used three trackability measures to evaluate the tracking object and trigger camera handoff accordingly, selecting the optimal camera for target tracking. The three measures considered were the resolution, occlusion, and distance to the edge of the camera's field of view. When one of these reached a preset threshold with an increasing trend, the system triggered the camera handoff.

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Section: Literature Review and Methodologymentioning

confidence: 99%

Camera Handoff for Multicamera Multiobject Tracking

Wang¹,

Sheu²,

Lai³

2022

Sensors and Materials

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“…In addition, there are some special person re-identification works [239]. Text-based retrieval [199], [200] is similar to pedestrian attribute recognition and person search.…”

Section: C: Low-resolution Person Re-identificationmentioning

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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“…It can quickly and efficiently detect and track target people in large-scale distributed surveillance networks [2][3][4]. Therefore, pedestrian reidentification technology has great scientific value and broadness in maintaining social stability, improving public safety, and protecting people's lives and property safety [5][6][7]. Zajdel et al [8] first proposed related concepts about pedestrian reidentification.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Reidentification Algorithm Based on Deconvolution Network Feature Extraction‐Multilayer Attention Mechanism Convolutional Neural Network

Liu

Lei

2021

Journal of Sensors

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Pedestrian reidentification is a key technology in large-scale distributed camera systems. It can quickly and efficiently detect and track target people in large-scale distributed surveillance networks. The existing traditional pedestrian reidentification methods have problems such as low recognition accuracy, low calculation efficiency, and weak adaptive ability. Pedestrian reidentification algorithms based on deep learning have been widely used in the field of pedestrian reidentification due to their strong adaptive ability and high recognition accuracy. However, the pedestrian recognition method based on deep learning has the following problems: first, during the learning process of the deep learning model, the initial value of the convolution kernel is usually randomly assigned, which makes the model learning process easily fall into a local optimum. The second is that the model parameter learning method based on the gradient descent method exhibits gradient dispersion. The third is that the information transfer of pedestrian reidentification sequence images is not considered. In view of these issues, this paper first examines the feature map matrix from the original image through a deconvolution neural network, uses it as a convolution kernel, and then performs layer-by-layer convolution and pooling operations. Then, the second derivative information of the error function is directly obtained without calculating the Hessian matrix, and the momentum coefficient is used to improve the convergence of the backpropagation, thereby suppressing the gradient dispersion phenomenon. At the same time, to solve the problem of information transfer of pedestrian reidentification sequence images, this paper proposes a memory network model based on a multilayer attention mechanism, which uses the network to effectively store image visual information and pedestrian behavior information, respectively. It can solve the problem of information transmission. Based on the above ideas, this paper proposes a pedestrian reidentification algorithm based on deconvolution network feature extraction-multilayer attention mechanism convolutional neural network. Experiments are performed on the related data sets using this algorithm and other major popular human reidentification algorithms. The results show that the pedestrian reidentification method proposed in this paper not only has strong adaptive ability but also has significantly improved average recognition accuracy and rank-1 matching rate compared with other mainstream methods.

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True-Color and Grayscale Video Person Re-Identification

Cited by 19 publications

References 45 publications

Camera Handoff for Multicamera Multiobject Tracking

Camera Handoff for Multicamera Multiobject Tracking

A Comprehensive Overview of Person Re-Identification Approaches

Pedestrian Reidentification Algorithm Based on Deconvolution Network Feature Extraction‐Multilayer Attention Mechanism Convolutional Neural Network

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