Training optimal Viola-Jones detectors using greedy algorithms for selecting control parameters with intermediate validation on each level
Nowadays object detection using video stream problem is considered solved. One of the most popular approaches to cope with this problem is Viola-Jones cascade learning. However our detection problem is appear to be highly complicated in practice due to variation of object form and lightness conditions in scene. In that case there are two ways to improve detection quality: trying to find another method or learning method improvement. In this paper we consider Viola-Jones learning parameter selection to optimize resulting detector in “Selectivity - Specificity - Productivity” space. We investigate greedy algorithm for selection learning parameters on each level. Also we state that it is possible to significantly improve cascade quality using intermediate level-wise checking under validation set. We provide paper by experimental results.