Human Action Recognition using Bio-inspired Visual Motion Estimation

Abstract

The emerging research work in human activity recognition is required for smart environment especially in health and home applications. This paper proposed a bio-inspired human visual system model to extract motion features for human activity recognition. The architecture of classical V1-MT model is modified into hierarchical architecture of feedforward model; (1) three-dimensional (3D) Gabor filter in primary visual cortex (V1) layer, (2) new pooling mechanism of active motion map (3) final motion estimation based on confidence mapping in MT layer. The 3D Gabor filter used to model the spatio-temporal direction and speed tuning of the V1 cells. The active mean motion map indicate the most significant feature vector in representing human actions, while the confidence map used to estimate accurate velocity characteristic of neurons in MT layer. The approach is carried out on the Weizmann and KTH action database. The results demonstrate the effectiveness of proposed method and its advantage in terms of recognition performance and computational efficiency compared to other bio-inspired approaches for human action recognition.