Guiding Visual Surveillance by Tracking Human Attention
Ben Benfold, Ian D. Reid · 2009
We describe a novel method for directing the attention of an automated surveillance system. Our starting premise is that the attention of people in a scene can be used as an indicator of interesting areas and events. To determine people’s attention from passive visual observations we develop a system for automatic tracking and detection of individual heads to infer their gaze direction. The former is achieved by combining a histogram of oriented gradient (HOG) based head detector with frame-to-frame tracking using multiple point features to provide stable head images. The latter is achieved using a head pose classification method which uses randomised ferns with decision branches based on both HOG and colour based features to determine a coarse gaze direction for each person in the scene. By building both static and temporally varying maps of areas where people look we are able to identify interesting regions.