智能系统学报
智能繫統學報
지능계통학보
CAAI TRANSACTIONS ON INTELLIGENT SYSTEMS
2013年
3期
189-198
,共10页
视频序列%人体运动描述%特征提取%特征选择%特征融合
視頻序列%人體運動描述%特徵提取%特徵選擇%特徵融閤
시빈서렬%인체운동묘술%특정제취%특정선택%특정융합
video sequences%human action representation%feature extraction%feature selection%feature fusion
视频中的人体运动分析是计算机视觉领域的重要课题,同时也是近年来备受关注的前沿研究方向之一。在明确实际视频中存在的若干种难点,如人体遮挡、视频模糊、拍摄视角变化等基础上,从经典的人体运动特征提取、特征选择以及特征融合3个方面,对基于视频序列的人体运动描述方法和研究现状进行了概述,归纳出人体运动描述算法的研究难点,并分析了人体运动分析的技术发展趋势。指出了利用不同特征间存在的互补性质探求高性能特征选择和特征融合机制是人体运动描述技术发展的必然趋势,从处理简单实验场景视频向挑战高难度实际场景视频的转化是运动视频分析未来发展的方向。
視頻中的人體運動分析是計算機視覺領域的重要課題,同時也是近年來備受關註的前沿研究方嚮之一。在明確實際視頻中存在的若榦種難點,如人體遮擋、視頻模糊、拍攝視角變化等基礎上,從經典的人體運動特徵提取、特徵選擇以及特徵融閤3箇方麵,對基于視頻序列的人體運動描述方法和研究現狀進行瞭概述,歸納齣人體運動描述算法的研究難點,併分析瞭人體運動分析的技術髮展趨勢。指齣瞭利用不同特徵間存在的互補性質探求高性能特徵選擇和特徵融閤機製是人體運動描述技術髮展的必然趨勢,從處理簡單實驗場景視頻嚮挑戰高難度實際場景視頻的轉化是運動視頻分析未來髮展的方嚮。
시빈중적인체운동분석시계산궤시각영역적중요과제,동시야시근년래비수관주적전연연구방향지일。재명학실제시빈중존재적약간충난점,여인체차당、시빈모호、박섭시각변화등기출상,종경전적인체운동특정제취、특정선택이급특정융합3개방면,대기우시빈서렬적인체운동묘술방법화연구현상진행료개술,귀납출인체운동묘술산법적연구난점,병분석료인체운동분석적기술발전추세。지출료이용불동특정간존재적호보성질탐구고성능특정선택화특정융합궤제시인체운동묘술기술발전적필연추세,종처리간단실험장경시빈향도전고난도실제장경시빈적전화시운동시빈분석미래발전적방향。
Recently analysis of human actions in videos has become an important issue in the field of computer vi -sion.Much attention has been paid to this frontier research .In this paper, we first explicitly defines several existing difficulties in real-world videos, such as body occlusion, video fuzzy, shooting angle change and then conducts a survey based on the popular methods and present situation research studies on human action representation .Next, we focus attention on three aspects of feature extraction , feature selection and feature fusion, and then summarize the research difficulties in algorithms of action description , and analyze the technical development trend of human action analysis.It was pointed out that the inevitable trend of human action representation technology is to explore high-performance feature selection and feature merging mechanism by making use of the complementary mechanism among different features, and the development trend of motion video analysis in the future is to change from pro-cessing simple experimental scene videos to the challenge of real-world scene videos with high difficulties .