Discriminative Figure-Centric Models for Joint Action Localization and Recognition

In this paper we develop an algorithm for action recognition and localization in videos. The algorithm uses a figurecentric visual word representation. Different from previous approaches it does not require reliable human detection and tracking as input. Instead, the person location is treated as a latent variable that is inferred simultaneously with action recognition. A spatial model for an action is learned in a discriminative fashion under a figure-centric representation. Temporal smoothness over video sequences is also enforced. We present results on the UCF-Sports dataset, verifying the effectiveness of our model in situations where detection and tracking of individuals is challenging.

• Type: Journal
• Year: 2011
• Where: ICCV
• Authors: Tian Lan, Yang Wang, Greg Mori
• Keywords: Action Recognition, Computer Vision, ICCV 2011, Ucf Sports, Video Sequences