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Sensors 2014, 14(6), 11245-11259; doi:10.3390/s140611245

Robust Pedestrian Tracking and Recognition from FLIR Video: A Unified Approach via Sparse Coding

1
Lane Department of CSEE, Morgantown, WV 26506-6109, USA
2
Department of EECS, University of Tennessee, Knoxville, TN 37996, USA
3
Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO 65211, USA
*
Author to whom correspondence should be addressed.
Received: 5 May 2014 / Revised: 3 June 2014 / Accepted: 13 June 2014 / Published: 24 June 2014
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Abstract

Sparse coding is an emerging method that has been successfully applied to both robust object tracking and recognition in the vision literature. In this paper, we propose to explore a sparse coding-based approach toward joint object tracking-and-recognition and explore its potential in the analysis of forward-looking infrared (FLIR) video to support nighttime machine vision systems. A key technical contribution of this work is to unify existing sparse coding-based approaches toward tracking and recognition under the same framework, so that they can benefit from each other in a closed-loop. On the one hand, tracking the same object through temporal frames allows us to achieve improved recognition performance through dynamical updating of template/dictionary and combining multiple recognition results; on the other hand, the recognition of individual objects facilitates the tracking of multiple objects (i.e., walking pedestrians), especially in the presence of occlusion within a crowded environment. We report experimental results on both the CASIAPedestrian Database and our own collected FLIR video database to demonstrate the effectiveness of the proposed joint tracking-and-recognition approach.
Keywords: robust tracking; pedestrian recognition; sparse coding; template updating; FLIR video robust tracking; pedestrian recognition; sparse coding; template updating; FLIR video
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Li, X.; Guo, R.; Chen, C. Robust Pedestrian Tracking and Recognition from FLIR Video: A Unified Approach via Sparse Coding. Sensors 2014, 14, 11245-11259.

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