TY - GEN
T1 - Fast Vehicle Detection with Lateral Convolutional Neural Network
AU - He, Chen Hang
AU - Lam, Kin Man
PY - 2018/4/15
Y1 - 2018/4/15
N2 - In this paper, we propose a fast vehicle detector for traffic surveillance. We first explore using different feature layers from a deep residual network to perform vehicle detection. Experiment results show that the high-resolution features from earlier feature layers contain more structural information, which is good to achieve fine-grained localization but yields low recall rates. The low-resolution features in the deep layers contain semantically strong information, which is good to represent the objectness but too coarse to achieve accurate localization. Therefore, we decouple the localization and objectness prediction from a single layer. Instead, we employ a lateral network that takes the features from earlier layers as input and outputs the localization residual. Our proposed detector can achieve fast detection at a rate of 28 frames/s, and a mean average precision (mAP) of 67.25% in the DETRAC vehicle detection benchmark.
AB - In this paper, we propose a fast vehicle detector for traffic surveillance. We first explore using different feature layers from a deep residual network to perform vehicle detection. Experiment results show that the high-resolution features from earlier feature layers contain more structural information, which is good to achieve fine-grained localization but yields low recall rates. The low-resolution features in the deep layers contain semantically strong information, which is good to represent the objectness but too coarse to achieve accurate localization. Therefore, we decouple the localization and objectness prediction from a single layer. Instead, we employ a lateral network that takes the features from earlier layers as input and outputs the localization residual. Our proposed detector can achieve fast detection at a rate of 28 frames/s, and a mean average precision (mAP) of 67.25% in the DETRAC vehicle detection benchmark.
KW - Convolutional neural network
KW - Vehicle detection
UR - http://www.scopus.com/inward/record.url?scp=85054226899&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2018.8461874
DO - 10.1109/ICASSP.2018.8461874
M3 - Conference article published in proceeding or book
AN - SCOPUS:85054226899
SN - 9781538646588
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2341
EP - 2345
BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Y2 - 15 April 2018 through 20 April 2018
ER -