Robust Human Action Recognition Using AREI Features and Trajectory Analysis from Silhouette Image Sequence

In the present work, we have proposed an efficient approach for human action recognition (HAR) from silhouette image sequence in videos. The efficiency of the approach lies in feature extraction and action classification. The proposed approach includes scale-shift normalization and distorted silhouette removal for the extraction of newly introduced spatiotemporal features coined as active region energy feature (AREF), and trajectory analysis. On the other hand, classification is done using hierarchical structure. An active region is the changing region in two consecutive silhouettes to accomplish the action. The AREF is estimated using active region energy image (AREI), which embraces the energies of active regions. The higher values in the AREI signify the more activeness (changing) of that region across the silhouette sequences; i.e. the region is used more (active) to complete the action. The silhouette normalization technique makes the feature extraction more robust and scale invariant. Also, the proposed approach uses a low-dimensional feature vector, which makes the whole procedure effective regarding cost in terms of timing requirement. The experimental results on publicly available Weizmann and MuHVAi data-sets clearly validate the efficiency of the proposed technique on that of the related research work regarding accuracy in the human action detection.