@article{ref1,
title="Robust pedestrian tracking and recognition from FLIR video: a unified approach via sparse coding",
journal="Sensors (Basel)",
year="2014",
author="Li, Xin and Guo, Rui and Chen, Chao",
volume="14",
number="6",
pages="11245-11259",
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.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s140611245",
url="http://dx.doi.org/10.3390/s140611245"
}