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.
Lan et al. REF propose a figure-centric representation for action localization and recognition by treating person location as a latent variable and inferring it while simultaneously recognizing the action.
Discriminative figure-centric models for joint action localization and recognition
2011 International Conference on Computer Vision
2011 International Conference on Computer Vision
Computer Science