Context Aware Group Activity Recognition

Abstract

This paper addresses the task of group activity recognition in multi-person videos. Existing approaches decompose this task into feature learning and relational reasoning. Despite showing progress, these methods only rely on appearance features for people and overlook the available contextual information, which can play an important role in group activity understanding. In this work, we focus on the feature learning aspect and propose a two-stream architecture that not only considers person-level appearance features, but also makes use of contextual information present in videos for group activity recognition. In particular, we propose to use two types of contextual information beneficial for two different scenarios: pose context and scene context that provide crucial cues for group activity understanding. We combine appearance and contextual features to encode each person with an enriched representation. Finally, these combined features are used in relational reasoning for predicting group activities. We evaluate our method on two benchmarks, Volleyball and Collective Activity and show that joint modeling of contextual information with appearance features benefits in group activity understanding.

Continuous Human Action Recognition for Human-Machine Interaction: A Review

“…A preliminary investigation towards the first pathway is presented in prior works [136], [137], [138]. For instance, in the group activity recognition setting person-level features and the scene-level features are incorporated into the action recognition model in [136] where as in [137] the authors extend this idea to utilise pose context as well. However, these works try to recognise actions that are already segmented videos.…”