TY - GEN
T1 - Visual saliency-based vehicle manufacturer recognition using autoencoder pre-training deep neural networks
AU - Zhang, Jiang
AU - Zhao, Yao
AU - Zhou, Fuqiang
AU - Chi, Ming
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Vehicle manufacturer recognition (VMR), consisting of vehicle logo detection (VLD) and vehicle logo recognition (VLR), is now a crucial part of intelligent transportation system (ITS). A novel VMR method combining visual saliency detection and autoencoder pre-training deep neural network (AP-DNN) is proposed in this paper. An automatic VLD method based on visual saliency detection is used to build a vehicle logo dataset. This dataset contains 10000 training samples and 1500 testing samples for ten types of vehicle manufacturers. In the experiment stage, using AP-DNN, a VLR rate of 99.20% with a training time of 40 min is obtained, which shows higher accuracy than scale-invariant feature transform (SIFT) or AdaBoost-based methods and less training time than methods using a convolutional neural network (CNN). Further, with 2000 vehicle images for ten different types of manufacturers, a VMR rate of 97.95% is obtained automatically and demonstrates the robustness and efficiency of our method.
AB - Vehicle manufacturer recognition (VMR), consisting of vehicle logo detection (VLD) and vehicle logo recognition (VLR), is now a crucial part of intelligent transportation system (ITS). A novel VMR method combining visual saliency detection and autoencoder pre-training deep neural network (AP-DNN) is proposed in this paper. An automatic VLD method based on visual saliency detection is used to build a vehicle logo dataset. This dataset contains 10000 training samples and 1500 testing samples for ten types of vehicle manufacturers. In the experiment stage, using AP-DNN, a VLR rate of 99.20% with a training time of 40 min is obtained, which shows higher accuracy than scale-invariant feature transform (SIFT) or AdaBoost-based methods and less training time than methods using a convolutional neural network (CNN). Further, with 2000 vehicle images for ten different types of manufacturers, a VMR rate of 97.95% is obtained automatically and demonstrates the robustness and efficiency of our method.
KW - Vehicle manufacturer recognition (VMR)
KW - deep learning
KW - intelligent transportation systems (ITS)
KW - visual saliency detection
UR - https://www.scopus.com/pages/publications/85049402808
U2 - 10.1109/IST.2017.8261506
DO - 10.1109/IST.2017.8261506
M3 - 会议稿件
AN - SCOPUS:85049402808
T3 - IST 2017 - IEEE International Conference on Imaging Systems and Techniques, Proceedings
SP - 1
EP - 6
BT - IST 2017 - IEEE International Conference on Imaging Systems and Techniques, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Imaging Systems and Techniques, IST 2017
Y2 - 18 October 2017 through 20 October 2017
ER -