IS = { zkontrolovano 12 Jan 2015 },
  UPDATE  = { 2015-01-12 },
author =      {Pokorn{\'y}, Jan and Trefn{\'y}, Ji{\v r}{\'\i} and Matas, 
Ji{\v r}{\'\i}},
title =       {Sharing local information in scanning-window detection},
institution = {Center for Machine Perception, K13133 FEE
               Czech Technical University},
address =     {Prague, Czech Republic},
year =        {2014},
month =       {December},
type =        {Research Report},
number =      {CTU--CMP--2014--22},
issn =        {1213-2365},
pages =       {11},
figures =     {6},
authorship =  {34-33-33},
psurl       = {[Pokorny-TR-2014-22.pdf]},
project =     {Toyota 8300322C},
annote =      {Object detection is a classic task in computer
                  vision. WaldBoost algorithm is a state-of-the-art
                  method for object detection due its high detection
                  accuracy and real-time speed. However, since the
                  traditional scanning window method classifies all
                  the windows independently and doesn't make use of
                  the information shared among overlapping windows,
                  there is still a possibility of a significant
                  speed-up by exploiting this property. We evaluate
                  number of scanning patterns and predictors for
                  spatially adjacent windows, inspired by work of
                  Hradi{\v s} et. al. Furthermore, we generalize this
                  idea from spatially adjacent widows to multiple
                  scales and propose {WaldBoost with Crosstalk
                  Prediction}. Evaluating on a state-of-the-art
                  dataset for face detection, we show that a
                  significant speed-up can be achieved with {WaldBoost
                  with Crosstalk Prediction} with no or a little loss
                  of precision, outperforming the reference method of
                  Hradi{\v s} et. al.},
keywords =    {Object detection, Sequential decison, WaldBoost,
                  Scanning window},
comment =     {Confidential.},