Abstract. We develop methods for action retrieval from surveillance video using contextual feature representations. The novelty of our proposed approach is two-fold. First, we introduce a new feature representation called the action context (AC) descriptor. The AC descriptor encodes information about not only the action of an individual person in the video, but also the behaviour of other people nearby. This feature representation is inspired by the fact that the context of what other people are doing provides very useful cues for recognizing the actions of each individual. Second, we formulate our problem as a retrieval/ranking task, which is different from previous work on action classification. We develop an action retrieval technique based on rank-SVM, a state-of-the-art approach for solving ranking problems. We apply our proposed approach on two real-world datasets. The first dataset consists of videos of multiple people performing several group activities. The second dataset consists of surveillance videos from a nursing home environment. Our experimental results show the advantage of using contextual information for disambiguating different actions and the benefit of using rank-SVMs instead of regular SVMs for video retrieval problems.
Lan et al. REF propose a slightly modified descriptor, the action context (AC) descriptor, which, unlike the STL descriptor, encodes the actions instead of the poses of people at nearby locations.
Retrieving actions in group contexts
in International Workshop on Sign Gesture Activity
Computer Science