A generative model for recognizing mixed group activities in still images

Recognizing multiple mixed group activities from one still image is not a hard problem for humans but remains highly challenging for computer recognition systems. When modelling interactions among multiple units (i.e., more than two groups or persons), the existing approaches tend to divide them into interactions between pairwise units. However, no mathematical evidence supports this transformation. Therefore, these approaches' performance is limited on images containing multiple activities. In this paper, we propose a generative model to provide a more reasonable interpretation for the mixed group activities contained in one image. We design a four level structure and convert the original intra-level interactions into inter-level interactions, in order to implement both interactions among multiple groups and interactions among multiple persons within a group. The proposed four-level structure makes our model more robust against the occlusion and overlap of the visible poses in images. Experimental results demonstrate that our model makes good interpretations for mixed group activities and outperforms the state-of-the-art methods on the Collective Activity Classification dataset.