A Hybrid Approach for Individual and Group Activity Analysis in Crowded Scene

This paper presents an efficient hybrid (top-down and bottom-up) framework for activity recognition based on analyzing group context in crowded scenes. The approach presented starts by discovering interacting groups of people using a graph based clustering algorithm. Given the interacting groups, a novel group context activity descriptor is computed that captures not only the focal person’s activity but also the behaviors of neighbors in the group. Finally, for a high-level of understanding of human activities, we propose a bottom-up approach using a random field model to encode activity relationships between people in the scene. We evaluate our approach on two public benchmark datasets and compare the utility of our proposed descriptor with other descriptors using the same baseline recognition framework. The results of both the steps show that our approach with the proposed descriptor achieves recognition rates comparable to state-of-the-art methods for activity recognition in crowded scenes.