Object Activity Scene Description, Construction and Recognition

Abstract

Action recognition is a critical task for social robots to meaningfully engage with their environment. 3D human skeleton-based action recognition is an attractive research area in recent years. Although, the existing approaches are good at action recognition, it is a great challenge to recognize a group of actions in an activity scene. To tackle this problem, at first, we partition the scene into several primitive actions (PAs) based upon motion attention mechanism. Then, the primitive actions are described by the trajectory vectors of corresponding joints. After that, motivated by text classification based on word embedding, we employ convolution neural network (CNN) to recognize activity scenes by considering motion of joints as "word" of activity. The experimental results on the scenes of human activity dataset show the efficiency of the proposed approach.

arXiv comment13 pages, 9 figures

References in corpus (4)

• Two-Stream Convolutional Networks for Action Recognition in Videos
• Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors
• VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
• GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection