@inproceedings{075e50ba6b9c46ccad6868530da38c43,
title = "Human pose tracking using online latent structured support vector machine",
abstract = "Tracking human poses in a video is a challenging problem and has numerous applications. The task is particularly difficult in realistic scenes because of several intrinsic and extrinsic factors, including complicated and fast movements, occlusions and lighting changes. We propose an online learning approach for tracking human poses using latent structured Support Vector Machine (SVM). The first frame in a video is used for training, in which body parts are initialized by users and tracking models are learned using latent structured SVM. The models are updated for each subsequent frame in the video sequence. To solve the occlusion problem, we formulate a Prize-Collecting Steiner tree (PCST) problem and use a branch-and-cut algorithm to refine the detection of body parts. Experiments using several challenging videos demonstrate that the proposed method outperforms two state-of-the-art methods.",
keywords = "Body parts, Human pose tracking, Latent structured SVM, Online learning",
author = "Hua, {Kai Lung} and Sari, {Irawati Nurmala} and Yeh, {Mei Chen}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 23rd International Conference on MultiMedia Modeling, MMM 2017 ; Conference date: 04-01-2017 Through 06-01-2017",
year = "2017",
doi = "10.1007/978-3-319-51811-4_51",
language = "English",
isbn = "9783319518107",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "626--637",
editor = "Laurent Amsaleg and Gudmundsson, {Gylfi Th{\'o}r} and Cathal Gurrin and J{\'o}nsson, {Bj{\"o}rn Th{\'o}r} and Shin{\textquoteright}ichi Satoh",
booktitle = "MultiMedia Modeling - 23rd International Conference, MMM 2017, Proceedings",
}