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).

Robust Multi-View Change Detection

LANZA, ALESSANDRO;DI STEFANO, LUIGI;
2007

Abstract

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).
Proceedings of the British Machine Vision Conference 2007
1
10
A. Lanza; L. Di Stefano; J. Berclaz; F. Fleuret; P. Fua
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/51257
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