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.
- Aggarwal, J.K. and Ryoo, M.S. 2011. Human activity analysis: A Review. ACM Computing Surveys (CSUR), v.43 n.3, p.1--43. Google ScholarDigital Library
- Niebles, J.C., Wang, H. and Fei-Fei, L. 2008. Unsupervised learning of human action categories using spatial-temporal words. International Journal of Computer Vision (IJCV) 79, 3(Sep). Google ScholarDigital Library
- Ryoo, M.S. and Aggarwal, J.K. 2009. Spatio-temporal relationship match: Video structure comparison for recognition of complex human activities. In ICCV.Google Scholar
- Mahadevan, V., Li,W., Bhalodia, V. and Vasconcelos, N. 2010. Anomaly Detection in Crowded Scenes. In IEEE Conference on Computer Vision and Pattern Recognition.Google Scholar
- Ni, B., Yan, S. and Kassim, A. 2009. Recognizing Human Group Activities with Localized Causalities. In IEEE Conference on Computer Vision and Pattern Recognition.Google Scholar
- Zhu, G., Yan, S., Han, T.X. and Xu, C. 2011. Generative group activity analysis with quaternion descriptor. In International Conference on Advances in Multimedia Modeling. Google ScholarDigital Library
- Lan, T., Wang, Y., Yang, W. and Mori, G. 2010. Beyond Actions: Discriminative Models for Contextual Group Activities. In Annual Conference on Neural Information Processing Systems.Google Scholar
- Cheng, Z., Qin, L., Huang, Q., Jiang, S. and Tian, Q. 2010. Group Activity Recognition by Gaussian Process Estimation. In International Conference on Pattern Recognition. Google ScholarDigital Library
- Rasmussen, C. E. and Williams, C. K. I.2006. Gaussian Processes for Machine Learning. The MIT Press. Google ScholarDigital Library
- Dalal, N. and Triggs, B. 2005. Histograms of oriented gradients for human detection. In IEEE Conference on Computer Vision and Pattern Recognition. Google ScholarDigital Library
Index Terms
- Human group activity analysis with fusion of motion and appearance information
Recommendations
Recognition of Human Group Activity for Video Analytics
Proceedings, Part II, of the 16th Pacific-Rim Conference on Advances in Multimedia Information Processing -- PCM 2015 - Volume 9315Human activity recognition is an important and challenging task for video content analysis and understanding. Individual activity recognition has been well studied recently. However, recognizing the activities of human group with more than three people ...
Human activity analysis: A review
Human activity recognition is an important area of computer vision research. Its applications include surveillance systems, patient monitoring systems, and a variety of systems that involve interactions between persons and electronic devices such as ...
Human Motion Analysis
Human motion analysis is receiving increasing attention from computer vision researchers. This interest is motivated by a wide spectrum of applications, such as athletic performance analysis, surveillance, man machine interfaces, content-based image ...
Comments