Human Pose Tracking Using Online Latent Structured Support Vector Machine
详细信息 查看全文
- 关键词：Human pose tracking ; Latent structured SVM ; Online learning ; Body parts
- 刊名：Lecture Notes in Computer Science
- 出版年：2017
- 出版时间：2017
- 年：2017
- 卷：10132
- 期：1
- 页码：626-637
- 丛书名：MultiMedia Modeling
- ISBN：978-3-319-51811-4
- 卷排序：10132
文摘
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.