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Proceedings Paper

Human suspicious activity recognition in thermal infrared video
Author(s): Jakir Hossen; Eddie Jacobs; Fahmida K. Chowdhury
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Paper Abstract

Detecting suspicious behaviors is important for surveillance and monitoring systems. In this paper, we investigate suspicious activity detection in thermal infrared imagery, where human motion can be easily detected from the background regardless of the lighting conditions and colors of the human clothing and surfaces. We use locally adaptive regression kernels (LARK) as patch descriptors, which capture the underlying local structure of the data exceedingly well, even in the presence of significant distortions. Patch descriptors are generated for each query patch and for each database patch. A statistical approach is used to match the query activity with the database to make the decision of suspicious activity. Human activity videos in different condition such as, walking, running, carrying a gun, crawling, and carrying backpack in different terrains were acquired using thermal infrared camera. These videos are used for training and performance evaluation of the algorithm. Experimental results show that the proposed approach achieves good performance in suspicious activity recognition.

Paper Details

Date Published: 7 October 2014
PDF: 8 pages
Proc. SPIE 9220, Infrared Sensors, Devices, and Applications IV, 92200E (7 October 2014); doi: 10.1117/12.2062053
Show Author Affiliations
Jakir Hossen, The Univ. of Memphis (United States)
Eddie Jacobs, The Univ. of Memphis (United States)
Fahmida K. Chowdhury, The Univ. of Memphis (United States)


Published in SPIE Proceedings Vol. 9220:
Infrared Sensors, Devices, and Applications IV
Paul D. LeVan; Ashok K. Sood; Priyalal Wijewarnasuriya; Arvind I. D'Souza, Editor(s)

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