A progressive hierarchical analysis model for collective activity recognition

We propose a progressive hierarchical analysis model to perceive the collective activities. Compared with previous activity recognition works, it not only recognizes the collective activities, but also perceives the location and the action category of each individual. At first, we perform the person temporal consistency detection procedure for each individual of the collective activities. A person detection network and conditional random field are used to receive the bounding box sequences of the activity participators. Then, we recognize the individual actions using the learned spatial features and the motion features based on LSTM. At last, the combination of the recognized person-level action category vector, the scene context features and the interaction Context features are used to recognize the collective activities. We evaluate the proposed approach on benchmark collective activity datasets. Extensive experiments demonstrate the effects of the progressive hierarchical analysis model.