Human Intrusion Detection using Texture Classification in Real- Time

The task of detecting people entering a sterile zone is a common scenario for visual surveillance systems. We propose a novel texture classifier to detect a person in a video frame without temporal information in realtime by identifying salient texture regions. An extension to this classifier by fusing it with simple motion information significantly outperforms standard motion tracking. Lower detection time can be achieved by combining texture classification with Kalman filtering. F1 measures are given for the i-LIDS sterile zone dataset of the UK Home Office. The fusion approach running on 10 frames per second gives the highest result of F1=0.92 for the 24 hour test dataset.