A Novel Approach for Human Action Recognition from Silhouette Images

In this work, we propose a novel human action recognition (HAR) technique for human silhouette sequence based on spatio-temporal body parts movement (STBPM) and action-code classification (ACC). STBPM feature is designed to accumulate the signature of the activity of several body parts to accomplish any action. ACC is a code-based classifier for HAR, which needs no training and the codes of any action is created by analyzing the STBPM features. The proposed approach is view independent except the top view and scale invariant. The experimental results on publicly available Weizmann, MuHVAi, and IXMAS datasets clearly show that our proposed technique outperforms the related research works in terms of accuracy in the human action detection.