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

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

1,* , 2
 and 3
Received: 5 May 2014; in revised form: 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 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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.

AMA Style

Li X, Guo R, Chen C. Robust Pedestrian Tracking and Recognition from FLIR Video: A Unified Approach via Sparse Coding. Sensors. 2014; 14(6):11245-11259.

Chicago/Turabian Style

Li, Xin; Guo, Rui; Chen, Chao. 2014. "Robust Pedestrian Tracking and Recognition from FLIR Video: A Unified Approach via Sparse Coding." Sensors 14, no. 6: 11245-11259.


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