This paper proposed a novel action recognition method based on multi-feature fusion.In this method,the spatial-temporal features and depth features were merged in a random forest framework.The human body joint coordinates obtained from depth image sequences were processed into displacement feature and part-center feature as two new depth features.We applied these two depth features to describe the three-dimension structure of human.We densely sampled the trajectories from RGB image sequences,and utilized the foreground detection approach to reduce the effect of complex background.Then spatial-temporal features were constructed by the Bag-of-Words model with trajectories from the foreground.Finally,the robust random forest framework fused both the spatial-temporal features and the depth features for recognizing human actions in RGB-D image sequences.Experimental results on MSR Daily Activity 3D dataset demonstrated the effectiveness of the proposed method.%提出一种基于多特征融合的动作识别方法,利用随机森林学习框架融合RGB-D图像序列中的深度特征和时空特征.从深度图像序列中获取人的关节点位置信息,利用关节点坐标提取两种新的深度特征——位移特征和部件中心特征,共同描述人体三维结构信息.从RGB 图像序列中提取稠密轨迹,保留前景内的轨迹排除背景干扰,利用词袋模型构建时空特征.最后,采用鲁棒高效的随机森林学习框架融合两种互补的特征.在MSR Daily Activity3D数据集上的实验结果表明,所提出的方法和特征能够有效地识别RGB-D图像序列中人的动作.
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