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

• Published in: Sensors (Basel, Switzerland) Vol. 14; no. 6; pp. 11245 - 11259
• Main Authors: Li, Xin, Guo, Rui, Chen, Chao
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 24.06.2014; MDPI
• Subjects: Biometrics; Coding; Dictionaries; Facial recognition technology; FLIR video; Infrared; Object recognition; Optimization; pedestrian recognition; Pedestrians; Recognition; robust tracking; Sensors; sparse coding; template updating; Tracking; United States > US
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s140611245

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.