Adaptive feature selection method for action recognition of human body in RGBD data

目前在RGBD视频的行为识别中,为了提高识别准确率,许多方法采用多特征融合的方式。通过实验分析发现,行为在特定特征上的分类效果好,但是多特征融合; 并不能体现个别特征的分类优势,同时融合后的特征维度很高,时空开销大。为了解决这个问题,提出了; RGBD人体行为识别中的自适应特征选择方法,通过随机森林和信息熵分析人体关节点判别力,以高判别力的人体关节点的数量作为特征选择的标准。通过该数量; 阈值的筛选,选择关节点特征或者关节点相对位置作为行为识别特征。实验结果表明,该方法相比于特征融合的算法,行为识别的准确率有了较大提高,超过了大部; 分算法的识别结果。Many methods adopt the technique of multi-feature fusion to improve the; recognition accuracy of RGBD ideo. Experimental analyses revealed that; the classification effect of certain behavior in some features is good;; however, multi-feature fusion cannot reflect the classification; superiority of certain features. Moreover, multi-feature fusion is; highly dimensional and considerably expensive in terms of time and; space. This research proposes an adaptive feature selection method for; RGBD human-action recognition to solve this problem. First, random; forest and information entropy were used to analyze the judgment ability; of the human joints, whereas the number of human joints with high; judgment ability were chosen as the feature selection criterion. By; screening the threshold number, either the joint feature or the relative; positions of the joints was used as the recognition feature of action.; Experimental results show that compared with multi-feature fusion, the; method significantly improved the accuracy of action recognition and; outperformed most other algorithms.国家自然科学基金项目; 福建省自然科学基金项目; 中医健康管理福建省2011协同创新中心项

Human Action Recognition via Fused Kinematic Structure and Surface Representation

Human action recognition from visual data has remained a challenging problem in the field of computer vision and pattern recognition. This dissertation introduces a new methodology for human action recognition using motion features extracted from kinematic structure, and shape features extracted from surface representation of human body. Motion features are used to provide sufficient information about human movement, whereas shape features are used to describe the structure of silhouette. These features are fused at the kernel level using Multikernel Learning (MKL) technique to enhance the overall performance of human action recognition. In fact, there are advantages in using multiple types of features for human action recognition, especially, if the features are complementary to each other (e.g. kinematic/motion features and shape features). For instance, challenging problems such as inter-class similarity among actions and performance variation, which cannot be resolved easily by using a single type of feature, can be handled by fusing multiple types of features. This dissertation presents a new method for representing the human body surface provided by depth map (3-D) using spherical harmonics representation. The advantage of using the spherical harmonics representation is to represent the whole body surface into a nite series of spherical harmonics coefficients. Furthermore, these series can be used to describe the pose of the body using the phase information encoded inside the coefficients. Another method for detecting/tracking distal limb segments using the kinematic structure is developed. The advantage of using the distal limb segments is to extract discriminative features that can provide sufficient and compact information to recognize human actions. Our experimental results show that the aforementioned methods for human action description are complementary to each other. Hence, combining both features can enhance the robustness of action recognition. In this context, a framework to fuse multiple features using MKL technique is developed. The experimental results show that this framework is promising in incorporating multiple features in different domain for automated recognition of human action