基于RGBD图像的非结构化人体活动检测
Unstructured Human Activity Detection from RGBD Images
摘要：
能够检测和识别人类活动对于包括个人辅助机器人在内的多种应用至关重要。在本文中，我们执行非结构化的人类活动在非结构化环境中的检测和识别。我们使用RGBD传感器（Microsoft Kinect）作为输入传感器，并根据人体姿势和运动以及图像和点云信息计算一组特征。我们的算法是基于分层最大熵马尔可夫模型（MEMM），它认为一个人的活动组成的一组子活动。我们使用动态规划方法推断出两层图结构。我们测试了我们的算法，检测和识别四个人在不同环境中（如厨房，客厅，办公室等）进行的十二种不同活动，并且即使在训练集中之前没有见过该人时也能实现良好的性能。
英文摘要：
Being able to detect and recognize human activities is essential for several applications, including personal assistive robotics. In this paper, we perform detection and recognition of unstructured human activity in unstructured environments. We use a RGBD sensor (Microsoft Kinect) as the input sensor, and compute a set of features based on human pose and motion, as well as based on image and pointcloud information. Our algorithm is based on a hierarchical maximum entropy Markov model (MEMM), which considers a person's activity as composed of a set of sub-activities. We infer the two-layered graph structure using a dynamic programming approach. We test our algorithm on detecting and recognizing twelve different activities performed by four people in different environments, such as a kitchen, a living room, an office, etc., and achieve good performance even when the person was not seen before in the training set.
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