Abstract
Real-time human activity recognition is essential for human-robot interactions for assisted healthy independent living. Most previous work in this area is performed on traditional two-dimensional (2D) videos and both global and local methods have been used. Since 2D videos are sensitive to changes of lighting condition, view angle, and scale, researchers begun to explore applications of 3D information in human activity understanding in recently years. Unfortunately, features that work well on 2D videos usually don't perform well on 3D videos and there is no consensus on what 3D features should be used. Here we propose a model of human activity recognition based on 3D movements of body joints. Our method has three steps, learning dictionaries of sparse codes of 3D movements of joints, sparse coding, and classification. In the first step, space-time volumes of 3D movements of body joints are obtained via dense sampling and independent component analysis is then performed to construct a dictionary of sparse codes for each activity. In the second step, the space-time volumes are projected to the dictionaries and a set of sparse histograms of the projection coefficients are constructed as feature representations of the activities. Finally, the sparse histograms are used as inputs to a support vector machine to recognize human activities. We tested this model on three databases of human activities and found that it outperforms the state-of-the-art algorithms. Thus, this model can be used for real-time human activity recognition in many applications.
Highlights
A smart environment is a place where humans and objects can interact and communicate with each other in a human-like way [1]
We propose to learn a set of dictionaries of sparse codes to represent the complex spatial-temporal relationships among body joints
We propose a method to learn a set of sparse codes that can be used to represent human activities
Summary
A smart environment is a place where humans and objects (including mobile robots) can interact and communicate with each other in a human-like way [1]. It has a wide range of applications in home and office work, health care, assistive living, and industrial operations. Most previous work in human activity understanding is performed on traditional 2D color images/videos and both global and local spatial-tempo features have been proposed (reviewed in [10,11,12]). Because it is difficult to deal with variations in 2D images/videos due to changes in lighting condition, view angle, and scale, researcher begun to explore applications of 3D information in human activity understanding [9]. In contrast to 2D images/videos, depth maps such as those acquired by the Microsoft Kinect system are related to object geometry and are independent of lighting conditions
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