Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks
Recently, Convolutional Neural Networks (ConvNets) have shown promising performances in many computer vision tasks, especially image based recognition. How to effectively use ConvNets for video based recognition is still an open problem. In this paper, we propose a compact, effective yet simple method to encode spatio temporal information carried in 3D skeleton sequences into multiple 2D images, referred to as Joint Trajectory Maps (JTM) and ConvNets are adopted to exploit the discriminative features for real time human action recognition. The proposed method has been evaluated on three public benchmarks, i.e. MSRC 12 Kinect gesture dataset (MSRC 12) G3D dataset and UTD multimodal human action dataset (UTD MHAD) and achieved the state of the art results.
ACM Multimedia