Improving human action recognition in videos is restricted by the inherent limitations of the visual data. In this paper, we take the depth information into consideration and construct a novel dataset of human daily actions. The proposed ACT4 2 dataset provides synchronized data from 4 views and 2 sources, aiming to facilitate the research of action analysis across multiple views and multiple sources. We also propose a new descriptor of depth information for action representation, which depicts the structural relations of spatiotemporal points within action volume using the distance information in depth data. In experimental validation, our descriptor obtains superior performance to the state-of-the-art action descriptors designed for color information, and more robust to viewpoint variations. The fusion of features from different sources is also discussed, and a simple but efficient method is presented to provide a baseline performance on the proposed dataset.

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Human activity analysis is an important and challenging task in video content analysis and understanding. In this paper, we focus on the activity of small human group, which involves countable persons and complex interactions. To cope with the variant number of participants and inherent interactions within the activity, we propose a hierarchical model with three layers to depict the characteristics at different granularities. In traditional methods, group activity is represented mainly based on motion information, such as human trajectories, but ignoring discriminative appearance information, e.g. the rough sketch of a pose style. In our approach, we take advantage of both the motion and the appearance information in the spatiotemporal activity context under the hierarchical model. These features are inhomogeneous. Therefore, we employ multiple kernel learning methods to fuse the features for group activity recognition. Experiments on a surveillance-like human group activity database demonstrate the validity of our approach and the recognition performance is promising.