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We present a multi-view change detection approach aimed at being robust with respect to common “disturbance factors” yielding image changes in realworld applications. Disturbance factors causing “slow” or “fast-and-global” image variations, such as light changes and dynamic adjustments of camera parameters (e.g. auto-exposure and auto-gain control), are dealt with by a proper single-view change detector run independently on each view. The computed change masks are then fused into a “synergy mask” defined into a common virtual top-view, so as to detect and filter-out “fast-and-local” image changes due to physical points lying on the ground surface (e.g. shadows cast by moving objects and light spots hitting the ground surface).