Representative Body Points on Top-View Depth Sequences for Daily Activity Recognition

In this paper, a novel feature for activity recognition from vertical top-view depth image sequences is firstly proposed. Most of previous works are focusing mainly on the side-view depth or color image sequences, which unfortunately may encounter occlusion problems. Therefore, top-view camera setting is adopted in our research. Based on the idea of computed tomography (CT) from medical imaging, the depth images are segmented to different layer along the transverse plane. The representative body points which are found from the centroids of the regions on each slice. And those points will be a meaningful descriptor for the activity posture. Dynamic time warping algorithm is also applied to address the different sequence length problem. Finally, a SVM classifier is trained to classify our activities. To verify our performance, a new Top-View 3D Daily Activity Dataset is constructed. In our experiments, a challenging cross-subject test is conducted, and the performance of our representative body points is demonstrated. The result shows that the accuracy can achieve up to 97%, which is promising while being compared with those from the state-of-the-art methods in the literature.