Human activity recognition based on spatial distribution of gradients at sub-levels of average energy silhouette images - 2016


The aim of this paper is to gift a unified framework for human action and activity recognition by analysing the effect of computation of spatial distribution of gradients (SDGs) on average energy silhouette images (AESIs). Primarily based on the analysis of SDGs computation at varied decomposition levels, a good approach to compute the SDGs is developed. The AESI is built for the illustration of the form of action and activity and these are the reflection of 3D pose into 2D cause. To explain the AESIs, the SDGs at various sub-levels and sum of the directional pixels (SDPs) variations is computed. The temporal content of the activity is computed through R–transform (RT). Finally, the shape computed through SDGs and SDPs, and temporal evidences through RT of the human body is fused along at the popularity stage, that results in a replacement powerful unified feature map model. The performance of the proposed framework is evaluated on three different publicly offered datasets i.e. Weizmann, KTH, and Ballet and the recognition accuracy is computed using hybrid classifier. The highest recognition accuracy achieved on these data sets is compared with the similar state-of-the-art techniques and demonstrate the superior performance.

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