Learning the fusion of multiple video analysis detectors

Desurmont, X. Lavigne, F. Meessen, J. Macq, Benoît [UCL]

This paper presents a new fusion scheme for enhancing the result quality based on the combination of multiple different detectors. We present a study showing the fusion of multiple video analysis detectors like "detecting unattended luggage" in video sequences. One of the problems is the time jitter between different detectors, i.e. typically one system can trigger an event several seconds before another one. Another issue is the computation of the adequate fusion of realigned events. We propose a fusion system that overcomes these problems by being able (i) in the learning stage to match off-line the ground truth events with the result of the detectors events using a dynamic programming scheme (ii) to learn the relation between ground truth and result (iii) to fusion in real-time the events from different detectors thanks to the learning stage in order to maximize the global quality of result. We show promising results by combining outputs of different video analysis detector technologies.