IS = { zkontrolovano 15 Jan 2014 },
  UPDATE  = { 2013-08-02 },
   author    = {Fojt{\r{u}}, {\v{S}}imon and Zimmermann, Karel and 
     Pajdla, Tom{\'{a}}{\v{s}} and Hlav{\'{a}}{\v{c}}, V{\'{a}}clav},
   language   = {English},
   title     = {Domain Adaptation for Sequential Detection},
   year      = {2013},
   month     = {June},
   pages     = {215--224},
   editor    = {K\"{a}m\"{a}r\"{a}inen, Joni-Kristian and Koskela, Markus},
   booktitle = {SCIA 2013: Proceedings of the 18th Scandinavian Conference on Image Analysis},
   publisher = {Springer},
   location  = {Heidelberg},
   address   = {Heidelberg, Germany},
   isbn      = {978-3-642-38885-9},
   series    = {Lecture Notes in Computer Science},
   volume    = {7944},
   book_pages= {733},
   annote    = {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
   keywords   = {domain adaptation, knowledge transfer, sequential decision},
   authorship = {40-40-10-10},
   project    = {SGS12/187/OHK3/3T/13, GACR P103/11/P700, TACR TA01031478 AUTMODO, FP7-ICT-247870 NIFTi},
   doi        = {10.1007/978-3-642-38886-6_21},
   psurl      = { PDF },
   www        = {http://link.springer.com/chapter/10.1007/978-3-642-38886-6_21},
   day        = { 17-20 },
   venue      = { Espoo, Finland },
   organization = { Aalto University, Finland },
   prestige   = {important},
   acceptance_ratio = {0.507},