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DOOMRED: A New Optimization Technique for Boosted Cascade Detectors on Enforced Training Set

Abstract

We propose a new method to optimize the completely-trained boosted cascade detector on an enforced training set. Recently, due to the accuracy and real-time characteristics of boosted cascade detectors like the Adaboost, a lot of variant algorithms have been proposed to enhance the performance given a fixed number of training data. And, most of algorithms assume that a given training set well exhibits the real world distributions of the target and non-target instances. However, this is seldom true in real situations, and thus often causes higher false-classification ratio. In this paper, to solve the optimization problem of completely trained boosted cascade detector on false-classified instances, we propose a new base hypothesis weight optimization algorithm called DOOMRED (Direct Optimization Of Margin for Rare Event Detection) using a mathematically derived error upper bound of boosting algorithms. We apply the proposed algorithm to a cascade structured frontal face detector trained by AdaBoost algorithm. Experimental results demonstrate that the proposed algorithm has competitive ability to maintain accuracy and real-time characteristic of the boosted cascade detector compared to those of other heuristic approaches while requiring reasonably small amount of optimization time.

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Correspondence to Kyoung Mu Lee.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Park, D.W., Lee, K.M. DOOMRED: A New Optimization Technique for Boosted Cascade Detectors on Enforced Training Set. EURASIP J. Adv. Signal Process. 2008, 183804 (2008). https://doi.org/10.1155/2008/183804

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Keywords

  • Event Detection
  • Competitive Ability
  • Heuristic Approach
  • Weight Optimization
  • Face Detector