- Research Article
- Open Access
DOOMRED: A New Optimization Technique for Boosted Cascade Detectors on Enforced Training Set
EURASIP Journal on Advances in Signal Processing volume 2008, Article number: 183804 (2008)
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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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
- Event Detection
- Competitive Ability
- Heuristic Approach
- Weight Optimization
- Face Detector