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Proceedings Paper

Target tracking and classification directly in compressive measurement for low quality videos
Author(s): Chiman Kwan; Bryan Chou; Jonathan Yang; Trac Tran
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Paper Abstract

Although compressive measurements save data storage and bandwidth usage, they are difficult to be used directly for target tracking and classification without pixel reconstruction. This is because the Gaussian random matrix destroys the target location information in the original video frames. This paper summarizes our research effort on target tracking and classification directly in the compressive measurement domain. We focus on one type of compressive measurement using pixel subsampling. That is, original pixels in video frames are randomly subsampled to generate the compressive measurements. Even in such special compressive sensing setting, conventional trackers do not work in a satisfactory manner. We propose a deep learning approach that integrates YOLO (You Only Look Once) and ResNet (residual network) for multiple target tracking and classification. YOLO is used for multiple target tracking and ResNet is for target classification. Extensive experiments using optical videos in the SENSIAC database demonstrated the efficacy of the proposed approach.

Paper Details

Date Published: 13 May 2019
PDF: 11 pages
Proc. SPIE 10995, Pattern Recognition and Tracking XXX, 1099505 (13 May 2019); doi: 10.1117/12.2518496
Show Author Affiliations
Chiman Kwan, Applied Research, LLC (United States)
Bryan Chou, Applied Research, LLC (United States)
Jonathan Yang, Applied Research, LLC (United States)
Trac Tran, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 10995:
Pattern Recognition and Tracking XXX
Mohammad S. Alam, Editor(s)

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