@inproceedings{4f159772b4534facae72cd371528cb5f,
title = "Boosting-like deep convolutional network for pedestrian detection",
abstract = "This paper proposes a boosting-like deep learning (BDL) framework for pedestrian detection. The fusion of handcrafted and deep learned features is considered to extract more effective representations. Due to overtraining on the limited training samples, over-fitting and convergence stability are two major problems of deep learning. We propose the boosting-like algorithm to enhance the system convergence stability through adjusting the updating rate according to the classification condition of samples in the training process. We theoretically give the derivation of our algorithm. Our approach achieves 15.85\% and 3.81\% reduction in the average miss rate compared with ACF and JointDeep on the largest Caltech dataset, respectively.",
keywords = "Boosting-like, Feature learning, Feedback propagation, Pedestrian detection",
author = "Lei Wang and Baochang Zhang and Wankou Yang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 10th Chinese Conference on Biometric Recognition, CCBR 2015 ; Conference date: 13-11-2015 Through 15-11-2015",
year = "2015",
doi = "10.1007/978-3-319-25417-3\_68",
language = "英语",
isbn = "9783319254166",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "581--588",
editor = "Jucheng Yang and Zhenan Sun and Shiguang Shan and Jinfeng Yang and Jianjiang Feng and Weishi Zheng",
booktitle = "Biometric Recognition - 10th Chinese Conference, CCBR 2015, Proceedings",
address = "德国",
}