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

Sensors

Xin Li¹  Rui Guo² 

[1] Lane Department of CSEE, Morgantown, WV 26506-6109, USA;Department of EECS, University of Tennessee, Knoxville, TN 37996, USA; E-Mail:

关键词: robust tracking;    pedestrian recognition;    sparse coding;    template updating;    FLIR video;   

DOI  :  10.3390/s140611245

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.