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| Content Provider | ACM Digital Library |
|---|---|
| Author | Cheng, Zhongwei Jiang, Shuqiang Yan, Shuicheng Qin, Lei Huang, Qingming Tian, Qi |
| Abstract | 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. |
| Starting Page | 1401 |
| Ending Page | 1404 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450306164 |
| DOI | 10.1145/2072298.2072025 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2011-11-28 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Human group activity Activity analysis Feature fusion |
| Content Type | Text |
| Resource Type | Article |
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