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Boosting-Based On-Road Obstacle Sensing Using Discriminative Weak Classifiers
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DOI：10.3390/s110404372.png)
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摘要
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This paper proposes an extension of the weak classifiers derived from the Haar-like features for their use in the Viola-Jones object detection system. These weak classifiers differ from the traditional single threshold ones, in that no specific threshold is needed and these classifiers give a more general solution to the non-trivial task of finding thresholds for the Haar-like features. The proposed quadratic discriminant analysis based extension prominently improves the ability of the weak classifiers to discriminate objects and non-objects. The proposed weak classifiers were evaluated by boosting a single stage classifier to detect rear of car. The experiments demonstrate that the object detector based on the proposed weak classifiers yields higher classification performance with less number of weak classifiers than the detector built with traditional single threshold weak classifiers.
Keyword:
weak classifiers
Haar-like features
AdaBoost
quadratic discriminant analysis
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期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
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引用论文
Haar-like features with optimally weighted rectangles for rapid object detection.png)
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