@inproceedings{f7b4606568164f1db9206b012986dccc,
title = "Deformable part model based hand detection against complex backgrounds",
abstract = "Hand detection is a challenging task in hand gesture recognition system and the detection results can be easily affected by changes in hand shapes, viewpoints, lightings or complex backgrounds.In order to detect and localize the human hands in static images against complex backgrounds, a hand detection method based on a mixture of multi-scale deformable part models is proposed in this paper, which is trained discriminatively using latent SVM and consists of three components each defined by a root filter and three part filters.The hands are detected in a feature pyramid in which the features are variants of HOG descriptors.The experimental results show that the proposed method is invariant to small deformations of hand gestures and the mixture model has a good performance on NUS hand gesture dataset - II.",
keywords = "Complex backgrounds, Deformable part model, HOG features, Hand detection, Latent SVM",
author = "Chunyu Zou and Yue Liu and Jiabin Wang and Huaqi Si",
note = "Publisher Copyright: {\textcopyright} Springer Science+Business Media Singapore 2016.; 11th Chinese Conference on Advances in Image and Graphics Technologies, IGTA 2016 ; Conference date: 08-07-2016 Through 09-07-2016",
year = "2016",
doi = "10.1007/978-981-10-2260-9\_17",
language = "English",
isbn = "9789811022593",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "149--159",
editor = "Tieniu Tan and Ran He and Guoping Wang and Xiaoru Yuan and Sheng Li and Shengjin Wang and Yue Liu",
booktitle = "Advances in Image and Graphics Technologies - 11th Chinese Conference, IGTA 2016, Proceedings",
address = "Germany",
}