{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T02:06:23Z","timestamp":1740103583641,"version":"3.37.3"},"reference-count":14,"publisher":"Wiley","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:p>Accompanying the growth of surveillance infrastructures, surveillance IP cameras mount up rapidly, crowding Internet of Things (IoT) with countless surveillance frames and increasing the need of person reidentification (Re-ID) in video searching for surveillance and forensic fields. In real scenarios, performance of current proposed Re-ID methods suffers from pose and viewpoint variations due to feature extraction containing background pixels and fixed feature selection strategy for pose and viewpoint variations. To deal with pose and viewpoint variations, we propose the color distribution pattern metric<jats:italic> (<\/jats:italic><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mi>C<\/mml:mi><mml:mi>D<\/mml:mi><mml:mi>P<\/mml:mi><mml:mi>M<\/mml:mi><\/mml:math><jats:italic>)<\/jats:italic> method, employing color distribution pattern (<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mi>C<\/mml:mi><mml:mi>D<\/mml:mi><mml:mi>P<\/mml:mi><\/mml:math>) for feature representation and SVM for classification. Different from other methods, <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\"><mml:mi>C<\/mml:mi><mml:mi>D<\/mml:mi><mml:mi>P<\/mml:mi><\/mml:math> does not extract features over a certain number of dense blocks and is free from varied pedestrian image resolutions and resizing distortion. Moreover, it provides more precise features with less background influences under different body types, severe pose variations, and viewpoint variations. Experimental results show that our <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\"><mml:mi>C<\/mml:mi><mml:mi>D<\/mml:mi><mml:mi>P<\/mml:mi><mml:mi>M<\/mml:mi><\/mml:math> method achieves state-of-the-art performance on both 3DPeS dataset and ImageLab Pedestrian Recognition dataset with 68.8% and 79.8% rank 1 accuracy, respectively, under the single-shot experimental setting.<\/jats:p>","DOI":"10.1155\/2017\/4089505","type":"journal-article","created":{"date-parts":[[2017,12,18]],"date-time":"2017-12-18T18:34:26Z","timestamp":1513622066000},"page":"1-11","source":"Crossref","is-referenced-by-count":0,"title":["Color Distribution Pattern Metric for Person Reidentification"],"prefix":"10.1155","volume":"2017","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8756-2257","authenticated-orcid":true,"given":"Yingsheng","family":"Ye","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China"}]},{"given":"Xingming","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China"}]},{"given":"Wing W. 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