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

This paper proposes a new approach to recognize human postures in realtime video sequences. 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. Consistent with the hierarchical model of the human visual system, sub-sampling techniques are used to represent the object by line segments at multiple resolution levels. The whole classification is described as a coarse to fine procedure. An average realtime recognition rate of 88% is achieved in the experiment. Compared to conventional convolution method, the proposed algorithm reduces the computation cycles by 10 – 100 times. This work sets the foundation for size and position invariant object recognition for the implementation of event-based vision systems