TY - GEN
T1 - Vehicle type classification and attribute prediction using multi-task RCNN
AU - Huo, Zhuoqun
AU - Xia, Yizhang
AU - Zhang, Bailing
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/13
Y1 - 2017/2/13
N2 - Vehicle classification is an important subject of study due to its significance in a number of areas including law enforcement, traffic surveillance, autonomous navigation, and transportation management. While numerous approaches have been proposed, few studies have been published with regard to the multi-view classification of vehicles captured in real surveillance. In this paper, we consider the multi-view classification of vehicles as an attribute prediction problem with views (rear, front, and side) as attributes. The corresponding multi-task learning is implemented in the Region-based Convolutional Neural Network (RCNN) framework, which classifies vehicle categories (car, truck, bus, and van) and predicts the attributes simultaneously. Experiments on a field-captured vehicle dataset provide satisfactory results, with approximate 83% accuracy for vehicle type classification and over 90% accuracy for attribute prediction.
AB - Vehicle classification is an important subject of study due to its significance in a number of areas including law enforcement, traffic surveillance, autonomous navigation, and transportation management. While numerous approaches have been proposed, few studies have been published with regard to the multi-view classification of vehicles captured in real surveillance. In this paper, we consider the multi-view classification of vehicles as an attribute prediction problem with views (rear, front, and side) as attributes. The corresponding multi-task learning is implemented in the Region-based Convolutional Neural Network (RCNN) framework, which classifies vehicle categories (car, truck, bus, and van) and predicts the attributes simultaneously. Experiments on a field-captured vehicle dataset provide satisfactory results, with approximate 83% accuracy for vehicle type classification and over 90% accuracy for attribute prediction.
KW - Vehicle type recognition
KW - attribute prediction
KW - convolutional neural networks
KW - multi-task learning
UR - http://www.scopus.com/inward/record.url?scp=85016014734&partnerID=8YFLogxK
U2 - 10.1109/CISP-BMEI.2016.7852774
DO - 10.1109/CISP-BMEI.2016.7852774
M3 - Conference Proceeding
AN - SCOPUS:85016014734
T3 - Proceedings - 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2016
SP - 564
EP - 569
BT - Proceedings - 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2016
Y2 - 15 October 2016 through 17 October 2016
ER -