In previous work, we developed the Illum-PF-MT, which is the PF-MT idea applied to the problem of tracking temporally and spatially varying illumination change. In many practical problems, the rate at which illumination changes varies over time. For e.g. when a car transitions from shadow to sunlight or vice-versa the rate of illumination change is much higher than when it is in shadow or in sunlight. One way to model illumination change in such problems is using a Gaussian random walk model with two values of the change covariance - a large covariance when a "transition" is detected and a much smaller one when "no transition" is detected. But to use such a model, one needs to first detect the transition. The transition is a natural one and so it happens gradually (unlike a sudden manual dimming of the light in the room) and thus existing change detection statistics which are designed only for sudden changes are unable to detect the transition. In this paper, we propose to use the recently proposed generalized ELL (gELL) idea which uses the tracked part of the change to detect it and hence detects such partially trackable changes very quickly. Since gELL detects much before loss of track occurs, one is able to transition to the "transition" model and back without ever losing track. Also, for the first time, we demonstrate the use of gELL in combination with the PF-MT algorithm which is more stable to model change than the original PF.
['Amit A. Kale', 'Namrata Vaswani']
Generalized ELL for detecting and tracking through illumination model changes