CAJ | 학술논문

探讨了连续视频流中的基于统计特征的场景变化检测问题,并研究了包括直方图距离、卡方距离和巴氏距离的3个场景度量方法,提出了更优的巴氏距离场景度量方法.该方法能计算并最大化高维空间中的多模式聚集特征向量距离,由于具有满足三角不等式和非奇性的特性,相对于其他两种方法,它提高了检测性能.实验比较了场景变化检测的精确和检索率,结果与分析一致.
탐토료련속시빈류중적기우통계특정적장경변화검측문제,병연구료포괄직방도거리、잡방거리화파씨거리적3개장경도량방법,제출료경우적파씨거리장경도량방법.해방법능계산병최대화고유공간중적다모식취집특정향량거리,유우구유만족삼각불등식화비기성적특성,상대우기타량충방법,타제고료검측성능.실험비교료장경변화검측적정학화검색솔,결과여분석일치.
This paper studies statistics based scene change detection in the video streaming scenario, and three scene fea-ture metrics including histogram distance, chi-square distance, and Bhattacharyya distance have been investigated. With the unique characteristics of triangular inequality and non-singularity, Bhattacharyya distance has been proposed as a viable scene change metrics. It outperforms much better than the other two in that it calculates and maximizes the feature vector distance between muhi-modal clusters in a hyper-sphere space. The experiments are conducted and the precision recall sta-tistics are compared, and the results support our analysis.