Live demonstration: A bio-inspired event-based size and position invariant human posture recognition algorithm

Abstract

We demonstrate a realtime human postures recognition platform. The algorithm employs temporal difference imaging between video sequences as input and then decompose the contour of the active object into vectorial line segments. A scheme based on simplified Line Segment Hausdorff Distance combined with projection histograms is proposed to achieve size and position invariance recognition. Inspired by the hierarchical model of human visual system, the whole classification is described as a coarse to fine procedure. 88% average realtime recognition rate is achieved in the experiment.