{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T01:57:28Z","timestamp":1762999048437,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2014,6,24]],"date-time":"2014-06-24T00:00:00Z","timestamp":1403568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s140611245","type":"journal-article","created":{"date-parts":[[2014,6,25]],"date-time":"2014-06-25T02:49:43Z","timestamp":1403664583000},"page":"11245-11259","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Robust Pedestrian Tracking and Recognition from FLIR Video: A Unified Approach via Sparse Coding"],"prefix":"10.3390","volume":"14","author":[{"given":"Xin","family":"Li","sequence":"first","affiliation":[{"name":"Lane Department of CSEE, Morgantown, WV 26506-6109, USA"}]},{"given":"Rui","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of EECS, University of Tennessee, Knoxville, TN 37996, USA"}]},{"given":"Chao","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO 65211, USA"}]}],"member":"1968","published-online":{"date-parts":[[2014,6,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1145\/1177352.1177355","article-title":"Object tracking: A survey","volume":"38","author":"Yilmaz","year":"2006","journal-title":"ACM Comput. 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