We propose a domain adaptation method for se- quential decision-making process, which is suitable for real- time processing. The work is motivated by applications in surveillance, where detectors must be adapted to new obser- vation conditions. We address the situation, where the new observation is related to the previous observation by a para- metric transformation. We propose a learning procedure, which reveals the hidden transformation between the old and new data. The transformation essentially allows to transfer the knowledge from the old data to the new one. We show that our method can achieve a 60% speedup in the training w.r.t. the baseline WaldBoost algorithm while outperforming it in precision.
['Šimon Fojtů', 'Karel Zimmermann', 'Tomas Pajdla', 'Václav Hlaváč']
Domain Adaptation for Sequential Detection