Person Re‐identification Across Multiple Non‐overlapping Cameras by Grouping Similarity Comparison Model

Tan, Feigang; Liu, Weiming; Huang, Ling; Zhang, Cong; Shi, Wei; Li, Yanshan

doi:10.1049/cje.2016.08.007

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…The application of topology-related theories and methods has opened up a new path for target tracking research, and many valuable research results have been obtained. For example, a topology estimation method of a camera network based on the blind distance was proposed [25]. By this method, the average speed of the moving target can be obtained by tracking the target trajectory in the camera FOV, and then the target arrival time can be estimated using the reidentification method among nonoverlapping cameras, which effectively improves the accuracy of the camera network topology estimation.…”

Section: Introductionmentioning

confidence: 99%

A Moving Target Tracking Framework Based on a Set and Its Topological Space

et al. 2023

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Moving target tracking is a technology that matches frames and images based on target characteristics. This technology is widely utilized in intelligent transportation, logistics transportation, public security, sports event broadcasting, and other fields. Existing research focuses primarily on improving target detection and tracking algorithms to improve target retrieval and tracking efficiency. However, the majority of studies focus on global and full-range retrieval. More importantly, in large video scenes with multiple camera collaborations, these methods rarely consider the efficiency of target retrieval and tracking. Based on relevant theories and methods of video GIS, set theory, and topology, in this paper, a set and its topology space covering road networks, cameras, videos, and key frames were constructed. Additionally, the positioning, tracking, and track representation of a moving target based on the set and its topology space were solved. Compared to the feature vector algorithm, video summarization and Meanshift algorithm, the experimental findings reveal that the target retrieval performance, algorithm stability, and robustness are improved.

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Section: Introductionmentioning

confidence: 99%

A Moving Target Tracking Framework Based on a Set and Its Topological Space

et al. 2023

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“…In the past few years, many efforts have been made in person re‐id, with most of them based on still image [ 1 ] . However, in the image‐based person re‐id, only a few still images are contained for each person, which is inadequate to overcome the problems of occlusion, viewpoint and pose change, and generate a robust pedestrian representation.…”

Section: Introductionmentioning

confidence: 99%

“…In the past few years, many efforts have been made in person re-id, with most of them based on still image [1] .…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Scheme for Video‐Based Person Re‐identification Using Lightweight PCANet and Handcrafted LOMO Features

et al. 2021

Chinese J of Electronics

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A two‐level hierarchical scheme for video‐based person re‐identification (re‐id) is presented, with the aim of learning a pedestrian appearance model through more complete walking cycle extraction. Specifically, given a video with consecutive frames, the objective of the first level is to detect the key frame with lightweight Convolutional neural network (CNN) of PCANet to reflect the summary of the video content. At the second level, on the basis of the detected key frame, the pedestrian walking cycle is extracted from the long video sequence. Moreover, local features of Local maximal occurrence (LOMO) of the walking cycle are extracted to represent the pedestrian' s appearance information. In contrast to the existing walking‐cycle‐based person re‐id approaches, the proposed scheme relaxes the limit on step number for a walking cycle, thus making it flexible and less affected by noisy frames. Experiments are conducted on two benchmark datasets: PRID 2011 and iLIDS‐VID. The experimental results demonstrate that our proposed scheme outperforms the six state‐of‐art video‐based re‐id methods, and is more robust to the severe video noises and variations in pose, lighting, and camera viewpoint.

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Person re-identification based on metric learning: a survey

Zou

Peng

et al. 2021

Multimed Tools Appl

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Person Re‐identification Across Multiple Non‐overlapping Cameras by Grouping Similarity Comparison Model

Cited by 4 publications

References 17 publications

A Moving Target Tracking Framework Based on a Set and Its Topological Space

A Moving Target Tracking Framework Based on a Set and Its Topological Space

A Hierarchical Scheme for Video‐Based Person Re‐identification Using Lightweight PCANet and Handcrafted LOMO Features

Person re-identification based on metric learning: a survey

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