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Person Re-identification in Videos by Analyzing Spatio-temporal Tubes

Permanent link
https://hdl.handle.net/10037/21082
DOI
https://doi.org/10.1007/s11042-020-09096-x
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Date
2020-06-23
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Author
Sekh, Arif Ahmed; Dogra, Debi Prasad; Choi, Heeseung; Chae, Seungho; Kim, Ig-Jae
Abstract
Typical person re-identification frameworks search for k best matches in a gallery of images that are often collected in varying conditions. The gallery usually contains image sequences for video re-identification applications. However, such a process is time consuming as video re-identification involves carrying out the matching process multiple times. In this paper, we propose a new method that extracts spatio-temporal frame sequences or tubes of moving persons and performs the re-identification in quick time. Initially, we apply a binary classifier to remove noisy images from the input query tube. In the next step, we use a key-pose detection-based query minimization technique. Finally, a hierarchical re-identification framework is proposed and used to rank the output tubes. Experiments with publicly available video re-identification datasets reveal that our framework is better than existing methods. It ranks the tubes with an average increase in the CMC accuracy of 6-8% across multiple datasets. Also, our method significantly reduces the number of false positives. A new video re-identification dataset, named Tube-based Re-identification Video Dataset (TRiViD), has been prepared with an aim to help the re-identification research community.
Publisher
Springer
Citation
Sekh AA, Dogra, Choi, Chae, Kim. Person Re-identification in Videos by Analyzing Spatio-temporal Tubes. Multimedia tools and applications. 2020
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  • Artikler, rapporter og annet (fysikk og teknologi) [1062]
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