Motion Sequence-Based Human Abnormality Detection Scheme for Smart Spaces
- 108 Downloads
Smart spaces represent an emerging new paradigm that encompasses diverse active research areas such as ubiquitous, grid and cloud computing. Hence, there are a wide variety of interesting issues and applications for smart spaces, and surveillance is one issue that has long received much attention. In many cases, human motion is one of the most important clues used in assessing a situation for surveillance purposes. In this paper, we propose a new human abnormality detection scheme for surveillance purposes. More specifically, we first present a motion sequence matching algorithm called Dynamic View Warping to represent specific motion characteristics. Secondly, we propose a matching speed-up technique called Dynamic Group Warping that establishes boundaries in Dynamic View Warping. Thirdly, we propose an indexing scheme for motion sequences and present K-NN search algorithm to efficiently and effectively find similar motion sequences. Our extensive experiments show that our proposed methods achieve outstanding performance.
KeywordsAbnormality detection Smart spaces Motion sequence matching Dynamic view warping Dynamic group warping Surveillance camera
Unable to display preview. Download preview PDF.
- 2.Balan, A. O., & Black, M. J. (2006). An adaptive appearance model approach for model based articulated objecttracking. In IEEE conference on computer vision and pattern recognition (pp. 758–765).Google Scholar
- 3.Yamato, J., Ohya, J., & Ishii, K. (1992). Recognizing human action in time-sequential images using hidden markov models. In IEEE conference on computer vision and pattern recognition (pp. 379–385).Google Scholar
- 7.Zhao, T., & Nevatia, R. (2003). Bayesian human segmentation in crowded situations. In IEEE conference on computer vision and pattern recognition (pp. II459–II466).Google Scholar
- 8.Breit H., Rigoll G. (2003) A flexible multimodal object tracking system. IEEE Conference on Image Processing 3: 133–136Google Scholar
- 9.Viola, P., Jones, M. J., & Snow, D. (2003). Detecting pedestrians using patterns of motion and appearance. In IEEE conference on computer vision (pp. 734–741).Google Scholar
- 10.Kasai, D., Aizawa, K., & Yamasaki, T. (2009). Retrieval of time-varying mesh and motion capture data using 2D video queries based on silhouette shape descriptors. In IEEE conference on multimedia and expo (pp. 854–857).Google Scholar
- 11.Yi, B., Jagadish, H., & Faloutsos, C. (1998). Efficient retrieval of similar time sequences under time warping. In International conference on data engineering (pp. 23–27).Google Scholar
- 12.Schuldt, C., Laptev, I., & Caputo, B. (2004). Recognizing human actions: A local svm approach. In International conference on pattern recognition (pp. 32–36).Google Scholar