基于视频信息的人体行为识别得到了越来越多的关注。针对人体行为的局部表达，提出了一种新的局部轮廓特征来描述人体的外观姿势，可以同时利用水平和竖直方向上的轮廓变化信息。该特征能有效区分不同动作，与轮廓起始点无关，具有平移、尺度和旋转不变性。针对该特征，提出了一种基于随机森林的两阶段分类方法，使用随机森林分类器对行为视频的局部轮廓进行初分类，并根据每个局部轮廓对应决策类的分类树数目占总分类树数目的比例，提出了一种基于袋外（OOB）数据误差加权投票准则的行为视频分类算法。在测试数据集上的实验结果证实了该方法的有效性。

Human action recognition in videos has attracted more and more attentions. In view of the local expression of human behavior, a novel local contour feature representing body posture is proposed, which can make full use of information of the contour variation along both horizontal and vertical direction. The proposed local feature can distinguish different actions and is invariant to translation, scaling, rotation and change of start point of human contour. A two stage classifying framework based on random forest is also proposed by using this novel local body contour feature. Random forest is employed to classify each frame of the test video. After that, a video classification method based on out of bag(OOB) error weighted voting strategy to recognize action video according to the ratio of decision trees belonging to each local contour to total decision trees is proposed. Experimental results on test data set prove the effectiveness of proposed method.