Video Tooning with Special Effects based on Human Motion Analysis

• Main Authors: Tse-Chih Chen, 陳澤智
• Other Authors: I-Cheng Chang
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/9cd2a7

碩士 === 國立東華大學 === 資訊工程學系 === 96 === The principal objective of this paper is to build a novel video tooning technology, and it contains two principal components: hierarchical video segmentation and video tooning with special effects. In the first part of the system, we through the human motion build a video structure, which includes Entry shot, Action shot, and Exit shot respectively. The Action shot is the core of the video, which is divided into Gimmickry period, Incubate period, and Shooting period. Through the video structure, the system can efficiently compute some parameters, and these parameters interact with special effects, which we call special effects parameters. In the second part, we use mean shift segmentation to segment the foreground object. And we use the periods of video structure to compute these special effects parameters separately and through these parameters, they interact with special effects. Furthermore, there are three interactions with the special effects, which include special effect interacting with environment, with other special effects and with attacker. The contribution of this paper includes three major components: (1) The system analyzes the human motion to build a video structure which cuts video by hierarchical video segmentation, dividing two layers into shots and periods. (2) The system can recognize human motion automatically and combine with the special effects in order to design the dynamic special effects. (3) Through the combination of the special effects and parameters, the system makes the three interactions— with attacker, environment, and other special effects. The system gets these interactions with special effects that make our results more interesting and vivid than traditional video tooning. Keywords: video tooning, special effects, hidden markov models, human motion recognition, mean shift segmentation