Kinematic joint descriptor and depth motion descriptor with convolutional neural networks for human action recognition
S. Sandhya Rani, G. Apparao Naidu, V. Usha Shree
Abstract
Human Action Recognition has gained a huge research interest due to its widespread applications in various fields. However, due to several challenges like noisy and occluded data, view-point variations, body sizes etc., still the action recognition remains a challenging task. Most of the existing action recognition methods focused on the single data type thereby the recognition system has limited performance. To improve the recognition performance, we have modeled a new approach for human action recognition from two different data types; they are depth images and skeleton joints . Two different descriptors are developed for action representation; they are Differential Depth Motion History Image for depth maps and Motion Kinematic Joint Descriptor for skeleton joints. To attain a discriminative feature set, we have trained three different Convolutional Neural Network Models and the results are fused for final action classification . Simulation is carried out over two public datasets and the obtained results indicate that the proposed approach outperforms state-of-art methods.