模式识别与人工智能
模式識彆與人工智能
모식식별여인공지능
Moshi Shibie yu Rengong Zhineng
2013年
3期
293-299
,共7页
蔺广逢%朱虹%范彩霞%张二虎
藺廣逢%硃虹%範綵霞%張二虎
린엄봉%주홍%범채하%장이호
空间分布特征%表面特征%动态特征%自回归滑动平均动态模型%结构融合
空間分佈特徵%錶麵特徵%動態特徵%自迴歸滑動平均動態模型%結構融閤
공간분포특정%표면특정%동태특정%자회귀활동평균동태모형%결구융합
Spatial Distribution Feature%Appearance Feature%Dynamic Feature%Autoregressive Moving Average (ARMA) Model%Structure Fusion
人体动作的表面特征和动态特征无统一性描述,导致无法精确地区分人体动作.文中提出基于空间分布特征的人体动作动态建模识别方法(DMRSD).利用相对极坐标划分特征的空间区域,统计局部区域非零信息点数目,形成空间分布特征描述表面特征.然后利用自回归滑动平均动态模型建模空间特征序列,形成模型参数特征描述动态时间结构特征.最后通过各参数特征的相似矩阵线性关系假设,结构融合表面特征和动态运动特征,形成统一性描述特征.用最近邻识别人体动作.在Weizmann和KTH库中比对当前方法的识别结果,文中方法获得较好的识别性能.
人體動作的錶麵特徵和動態特徵無統一性描述,導緻無法精確地區分人體動作.文中提齣基于空間分佈特徵的人體動作動態建模識彆方法(DMRSD).利用相對極坐標劃分特徵的空間區域,統計跼部區域非零信息點數目,形成空間分佈特徵描述錶麵特徵.然後利用自迴歸滑動平均動態模型建模空間特徵序列,形成模型參數特徵描述動態時間結構特徵.最後通過各參數特徵的相似矩陣線性關繫假設,結構融閤錶麵特徵和動態運動特徵,形成統一性描述特徵.用最近鄰識彆人體動作.在Weizmann和KTH庫中比對噹前方法的識彆結果,文中方法穫得較好的識彆性能.
인체동작적표면특정화동태특정무통일성묘술,도치무법정학지구분인체동작.문중제출기우공간분포특정적인체동작동태건모식별방법(DMRSD).이용상대겁좌표화분특정적공간구역,통계국부구역비령신식점수목,형성공간분포특정묘술표면특정.연후이용자회귀활동평균동태모형건모공간특정서렬,형성모형삼수특정묘술동태시간결구특정.최후통과각삼수특정적상사구진선성관계가설,결구융합표면특정화동태운동특정,형성통일성묘술특정.용최근린식별인체동작.재Weizmann화KTH고중비대당전방법적식별결과,문중방법획득교호적식별성능.
@@@@The appearance feature and dynamic feature of human action have not an integrate description, which leads to distinguish human action inaccurately. In this paper, human action dynamic modeling recognition based on the spatial distribution feature (DMRSD) is proposed. Firstly, the spatial region of the feature is divided into a number of local regions by the relative polar coordinates, the statistic number of the nonzero information points is obtained in these local regions, and these numbers form a spatial distribution feature which describes the action appearance feature. Then, these spatial distribution feature sequences are modeled by autoregressive moving average model, then the feature of model parameter is obtained, which represents the dynamic time structure. Finally, the linear relation of the affinity matrix of these parameter features is hypothesized, the appearance feature structure and the dynamic motion feature structure are fused, and an integrate description is generated. Human action recognition is directly performed on the fusion structure of an integrate description by the nearest neighbor classification. Compared to the recognition results of current methods, DMRSD obtains better recognition rate on Weizmann and KTH databases.