Discovering Unusual Behavior Patterns from Motion Data

• Main Authors: Kai-LinPang, 龐凱齡
• Other Authors: Wei-Guang Teng
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/25192086288873642319

碩士 === 國立成功大學 === 工程科學系 === 102 === As there are more and more surveillance cameras installed in public places, a challenging problem is to discover unusual behavior patterns from a huge amount of video data. However, this task is currently only feasible for human beings because both object recognition and intention detection are still difficult for computer vision algorithms. Recently, with the release of low-cost depth cameras, motion data containing 3D coordinates of skeleton joints can be directly captured, thus facilitating following analysis tasks. In this work, we devise a complete system flow which includes steps of coordinate transformation, normalization, segmentation, feature extraction and dimensionality reduction so as to achieve the purpose of discovering unusual behavior patterns. Note that prior works generally require to predefine (either normal or abnormal) behavior patterns and then utilize data classification techniques for further analysis. Instead, we propose in this work to utilize data clustering techniques to discover unusual behavior patterns so that the inconsistencies of defining behavior patterns in various scenarios can be eased. Specifically, we adopt a density-based clustering technique and adjust the values of two parameters (i.e., radius and minimum points) to appropriately generate unusual behavior patterns. Finally, two datasets including MSR action recognition and AffectME (affective multimodal engagement) are used in our experiments for evaluation purposes. Empirical studies show that our approach is effective for discovering unusual behavior patterns.