Sparse Representation of Vehicle Image and Its' Application in Surveillance Video

doi:10.13190/j.jbupt.2016.s.019

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2016, Vol. 39 ›› Issue (s1): 81-86.doi: 10.13190/j.jbupt.2016.s.019

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Sparse Representation of Vehicle Image and Its' Application in Surveillance Video

CHEN Xiang-jun^1,2, RUAN Ya-duan¹, CHEN Qi-mei¹, YE Fei-yue²

1. School of Electronic Science and Engineering, Nanjing University, Nanjing 210046, China;
2. School of Computer Engineering, Jiangsu University of Technology, Jiangsu Changzhou 213001, China

Received:2015-09-06 Online:2016-06-28 Published:2016-06-28

Abstract

Abstract:

Typical vehicle image feature will lost robustness and generalization ability under complex scene. To deal with this problem, sparse based vehicle images feature representation was introduced and a linear vehicles support vector machine classifier based on the sparse representation was proposed. Then, a framework of vehicle classification and recognition on surveillance video was constructed based on the background subtraction and sparse represented feature. Compared with traditional methods, vehicle images are represented as linear combination of the sparse coefficient of a learned dictionary (atom or base) in low dimension in our method, and sparse represented feature gains higher generalization capability with less computational complexity. Experiment shows that this work exhibits better classification accuracy and robustness under complex real environment with decrease image quality of low resolution, shadow and occlusion.

Key words: feature representation, sparse learning, vehicle classification and recognition, robustness and generalization, intelligent transportation system

CLC Number:

TP391.4

CHEN Xiang-jun, RUAN Ya-duan, CHEN Qi-mei, YE Fei-yue. Sparse Representation of Vehicle Image and Its' Application in Surveillance Video[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2016, 39(s1): 81-86.

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Sparse Representation of Vehicle Image and Its' Application in Surveillance Video

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