光电工程
光電工程
광전공정
OPTO-ELECTRONIC ENGINEERING
2013年
3期
108-114
,共7页
王宪%慕鑫%宋书林%陈向阳
王憲%慕鑫%宋書林%陳嚮暘
왕헌%모흠%송서림%진향양
行为识别%局部二值模式%K-means聚类%隐马尔科夫模型
行為識彆%跼部二值模式%K-means聚類%隱馬爾科伕模型
행위식별%국부이치모식%K-means취류%은마이과부모형
human action recognition%LBP%K-means%HMM
视频序列中的行为分析与识别已经成为当前计算机视觉领域的研究热点.为了更加有效地提取人体行为序列中的轮廓特征的信息,提出了一种基于局部二值模式(Local Binary Pattern,LBP)特征的人体行为识别的算法.通过背景差分法从视频中提取完整的人体运动序列,利用LBP算子计算运动序列的LBP特征谱,组成样本的LBP轮廓特征空间,接着将特征空间通过K-means聚类的方法生成行为特征.最后,采用隐马尔可夫模型(HMM)对特征进行识别.实验过程中,分别在两个公共行为数据库上进行了测试实验,平均识别率能达到85%以上,并且在两个数据库的交叉实验结果表明了本文算法具有一定的鲁棒性.
視頻序列中的行為分析與識彆已經成為噹前計算機視覺領域的研究熱點.為瞭更加有效地提取人體行為序列中的輪廓特徵的信息,提齣瞭一種基于跼部二值模式(Local Binary Pattern,LBP)特徵的人體行為識彆的算法.通過揹景差分法從視頻中提取完整的人體運動序列,利用LBP算子計算運動序列的LBP特徵譜,組成樣本的LBP輪廓特徵空間,接著將特徵空間通過K-means聚類的方法生成行為特徵.最後,採用隱馬爾可伕模型(HMM)對特徵進行識彆.實驗過程中,分彆在兩箇公共行為數據庫上進行瞭測試實驗,平均識彆率能達到85%以上,併且在兩箇數據庫的交扠實驗結果錶明瞭本文算法具有一定的魯棒性.
시빈서렬중적행위분석여식별이경성위당전계산궤시각영역적연구열점.위료경가유효지제취인체행위서렬중적륜곽특정적신식,제출료일충기우국부이치모식(Local Binary Pattern,LBP)특정적인체행위식별적산법.통과배경차분법종시빈중제취완정적인체운동서렬,이용LBP산자계산운동서렬적LBP특정보,조성양본적LBP륜곽특정공간,접착장특정공간통과K-means취류적방법생성행위특정.최후,채용은마이가부모형(HMM)대특정진행식별.실험과정중,분별재량개공공행위수거고상진행료측시실험,평균식별솔능체도85%이상,병차재량개수거고적교차실험결과표명료본문산법구유일정적로봉성.
Human action recognition in the video sequence have become a hot research topic in computer vision field. In order to extract the contour feature of the human’s behavior sequence more effectively, a new algorithm for human action recognition based on Local Binary Pattern (LBP) is proposed. Firstly, background subtraction algorithm is used to extract the complete human motion sequence in the video, and the LBP operators are used to calculate the samples’ LBP feature space which is composed of the motion sequences’ LBP feature spectrum. Then, the behavior feature is generated by k-means clustering method. Finally, the Hidden Markov Model (HMM) is adopted for the classification. During the experiment, the test experiment is performed in the two public behavior databases respectively, and the average recognition rate can reach more than 85%. The intersection of the two databases experimental results shows that the proposed algorithm has certain robustness.