Domain Adaptation for Sequential Detection

We propose a domain adaptation method for sequential decision-making process. While most of the state-of-the-art approaches focus on SVM detectors, we propose the domain adaptation method for the sequential detector similar to WaldBoost, which is suitable for real-time processing. The work is motivated by applications in surveillance, where detectors must be adapted to new observation conditions. We address the situation, where the new observation is related to the previous observation by a parametric 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.