View Invariant Human Action Recognition using Surface Maps via convolutional networks
D. Anil Kumar, P. V. V. Kishore, G V K Murthy, Tangudu Ram Chaitanya, S K Mahaboob Subhani · 2023
In action recognition, varied views are very challenging task due to same action appeared in different from different views. To address these problems, we proposed a novel ActionNet framework for perspective view invariant human action recognition system based on convolutional neural networks (CNNs) trained with multi-view dataset captured by 5 depth cameras. Recently, maps are cauterized geometric feature like joint locations, distance, angle, velocity, or combination of features used for skeleton based action recognition. Against their success, features represent earlier find complex in representing a relative variation in 3D actions. Hence, we introduced a novel spatiotemporal color-coded image maps called a joint relational surface maps (JRSMs). JRSMs are calculated subset of three joints in a sequential order covering all joints. In literature, single view depth data with multi steam CNN to recognize human actions but cannot recognize accurately view invariant actions. In this work, we trained by multi view action data with single stream deep CNN model for recognizing view invariant actions. To test the performance of proposed architecture, we compare our results with other state-of-the-art action recognition architecture by using our own multi-view 3D skeleton action dataset named as KLU3DAction and two benchmark skeleton action datasets like NTU RGB-D and PKU-MMD.