title: Robust pedestrian tracking and recognition from FLIR video: a unified approach via sparse coding.
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.
authors: Lane Department of CSEE, Morgantown, WV 26506-6109, USA. xin.li@ieee.org.
authors: Xin
authors: X
authors: Li
authors: Department of EECS, University of Tennessee, Knoxville, TN 37996, USA. rguo1@utk.edu.
authors: Rui
authors: R
authors: Guo
authors: Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO 65211, USA. ccwwf@mail.missouri.edu.
authors: Chao
authors: C
authors: Chen