@inproceedings{7d56e2b629024e6ea3a11225a783698d,
title = "Pedestrian detection with D-CNN",
abstract = "Pedestrian detection plays an important role in intelligent analysis of images and videos. In this paper, we propose a deformation model based convolutional neural network(D-CNN) for pedestrian detection. Enlightened by YOLO model, D-CNN network integrates deformation and occlusion handling into the network to improve the accuracy of occluded pedestrian detection. The performance of D-CNN is evaluated on two popular datasets as well as pictures got in daily life. Among the state-of-the-art methods compared in this paper, the comprehensive performance of D-CNN is the best, whose mAP is only 0.4 points lower than the highest one but the detection speed doubles. So our proposed network can get real-time speed while maintaining rather satisfying precision of pedestrian detection.",
keywords = "D-CNN, Deformation handling, Occlusion handling, Pedestrian detection",
author = "Zhonghua Gao and Weiting Chen and Guitao Cao and Peng Chen",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Singapore Pte Ltd.; 15th National Conference on Embedded Systems Technology, ESTC 2017 ; Conference date: 17-11-2017 Through 19-11-2017",
year = "2018",
doi = "10.1007/978-981-13-1026-3\_13",
language = "英语",
isbn = "9789811310256",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "171--180",
editor = "Yuanguo Bi and Gang Chen and Qingxu Deng and Yi Wang",
booktitle = "Embedded Systems Technology - 15th National Conference, ESTC 2017, Revised Selected Papers",
address = "德国",
}