Illumination invariant tracking in office environments using neurobiology-saliency based particle filter
Background subtraction is a commonly employed approach for tracking in scenarios where the ambience is more or less constant in terms of illumination and number of objects. However in office environments, where the illumination can very easily change by switching off or on lights, the background subtraction method can lead to erroneous tracking. In this paper we propose a neurobiology-saliency based particle filter approach that uses low-level features like color, luminance and edge information along with motion cues to track a single person. We have tested our method on clips showing a single person carrying out a range of activities expected in an office environment. Our method performs better than a background subtraction method using a Kalman filter, in terms of the number of frames showing correct tracking and change detection for automatic initialization of tracks.