A SHOT BOUNDARY DETECTION METHOD FOR NEWS VIDEO BASED ON OBJECT SEGMENTATION AND TRACKING
As a critical step in many multimedia applications, shot boundary detection has attracted many research interests in recent years. The most of existing methods measure the similarity among video frames based on its low-level feathers. However, they are sensitive to the change in not only brightness, color, motion of object, but also camera motions and the quality of video. This paper proposes an innovative shot boundary detection method for news video based on video object segmentation and racking. It combines three main techniques: the partitioned histogram comparison method, the video object segmentation and tracking based on wavelet analysis. The partitioned histogram comparison is used as the first filter to effectively reduce the number of video frames which need object segmentation and tracking. The unsupervised video object segmentation and tracking based on wavelet analysis is robust to those problems mentioned above. The efficacy of the proposed method is extensively tested with more than 3 hours of CCTV and CNN news programs, and that 96.4％ recall with 97.2％ precision have been achieved.
Shot boundary detection Video object segmentation Object tracking partitioned histogram
XIN-WEN XU GUO-HUI LI JIAN YUAN
Information Integration & Training Simulation Lab, Department of System Engineering, School of Information system and Management, National University of Defense Technology, Changsha, Hunan, 410073, China
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
2470-2475
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）