Human detection in outdoor scene using spatio-temporal motion analysis

We propose an image processing algorithm for detecting human in outdoor scenes containing changeful background. In this work, regions extracted through background subtraction procedure are accurately classified into human and others by motion analysis in the three dimensional feature space constructed by the spatial uniqueness of image motion F₁, the temporal uniqueness of image motion F₂, and the temporal motion continuity F₃. Evaluation test proved that proposed algorithm could reduce the error rates of both false positive and false negative to about 1/3 compared with a conventional method. We also tested it in a PC-based real-time system over two weeks in real environments, that resulted in its false negative error rate of less than 1% and false positive error number of less than 3 times per day.