CAJ | 학술논문

　　针对视觉系统在动作识别过程中如何利用形状与运动信息的问题，提出了一种融合形状特征和运动特征的人体动作识别方法。该方法模拟视觉皮层的背侧和腹侧通路，建立了基于双通道理论的人体动作特征计算模型。计算模型分别利用2D Gabor滤波器和3D时空滤波器模拟腹侧和背侧通路中视觉皮层简单细胞，提取动作的时空信息，通过采样、局部遍历、模板学习一系列操作分别提取动作的时空特征，并采用线性融合方法获取描述动作的特征向量，构建了采用支持向量机( SVM)进行动作分类的动作识别系统。实验结果表明：该方法的识别性能优于同类型的识别方法，取得了较好的识别效果。
　　침대시각계통재동작식별과정중여하이용형상여운동신식적문제，제출료일충융합형상특정화운동특정적인체동작식별방법。해방법모의시각피층적배측화복측통로，건립료기우쌍통도이론적인체동작특정계산모형。계산모형분별이용2D Gabor려파기화3D시공려파기모의복측화배측통로중시각피층간단세포，제취동작적시공신식，통과채양、국부편력、모판학습일계렬조작분별제취동작적시공특정，병채용선성융합방법획취묘술동작적특정향량，구건료채용지지향량궤( SVM)진행동작분류적동작식별계통。실험결과표명：해방법적식별성능우우동류형적식별방법，취득료교호적식별효과。
This paper presents a computational model for human action recognition, which combined the form and motion features. This method mainly simulates the visual system's workflow about how to take use of form and motion information during the process of motion recognition. It established the computational model based on the theory of two channels by simulating the dorsal and ventral streams. In order to get the form and motion information,2D gabor filters and 3D spatial time filters are used separately to simulate the simple cells in the visual cortex. Then the same processing pipeline is applied in both channels: feature maps are pooled locally, down-sampled, and compared with a set of learnt templates, yielding a vector of similarity scores. In the final step, the two score vectors are merged and the final feature vector describing the human action will be achieved. It established the action recognition systems for action classification with support vector machine ( SVM) . The result shows that our method outperforms the similar existing methods,and gets better recognition.