1. SUMMERY 1

2. VOLUMETRIC FEATURES FOR EVENT DETECTION IN VIDEO correlate spatio-temporal shapes to video clips that have been automatically segmented we do not require background subtraction The first step is to extract spatio-temporal shape contours in the video using an unsupervised clustering technique. This enables us to ignore highly variable and potentially irrelevant features of the video such as color and texture, while preserving the object boundaries needed for shape classification. 2

3. VOLUMETRIC FEATURES FOR EVENT DETECTION IN VIDEO Our shape matching metric is based on the region intersection distance between the template volume and the set of over- segmented volumes in the video. The shape template can be efficiently scanned over the video and events are detected when the matching distance falls below a specified threshold. http://www.ri.cmu.edu/research_project_detail.html?project_id= 624&menu_id=261http://www.ri.cmu.edu/research_project_detail.html?project_id= 624&menu_id=261 3

4. VOLUMETRIC FEATURES FOR EVENT DETECTION IN VIDEO 4

5. HUMAN HAIR SEGMENTATION AND LENGTH DETECTION FOR HUMAN APPEARANCE MODEL able to segment hairs from different views of human heads even with low resolution. no face detection is needed in this method. detects human heads with a trained human head detector. Next, histogram analysis is carried out on the detected head region to segment hair region with K-mean clustering. Finally, hair length is determined by performing line scanning on the segmented hair region. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6976797 5

6. DATABASE https://www.eecs.berkeley.edu/Research/Projects/CS/vision/gr ouping/resources.htmlhttps://www.eecs.berkeley.edu/Research/Projects/CS/vision/gr ouping/resources.html 6