Multi-label Discriminative Weakly-Supervised Human Activity Recognition and Localization

Activity recognition in video has become increasingly important due to its many applications ranging from in-home elder care, surveillance, human computer interaction to automatic sports commentary. To date, most approaches to video rely on fully supervised settings that require time consuming and error prone manual labeling. Moreover, existing supervised approaches are typically tailored for classification, not detection problems (the spatial and temporal support of the action has to be detected). Recently, weakly-supervised learning (WSL) approaches were able to learn discriminative classifiers while localizing the action in space and/or time using weak labels. However, existing approaches for WSL provide coarse localization in terms of spatial regions or spatio-temporal volumes. Moreover, it is unclear how to extend current approaches to the multilabel case that is common in practical applications. This paper proposes a matrix completion approach to the problem of WSL for multi-label learning for video. Our approach localizes non-rectangular spatio-temporal discriminative regions that are inferred by clustering regions of common texture and motion features. We illustrate how our approach improves existing WSL and supervised learning techniques in three standard databases: Hollywood, UCF sports, and MSR-II.