Abstract
Automatic detection systems usually require large and representative training datasets in order to obtain good detection and false positive rates. Training datasets are such that the positive set has few samples and/or the negative set should represent anything except the object of interest. In this respect, the negative set typically contains orders of magnitude more images than the positive set. However, imbalanced training databases lead to biased classifiers. In this paper, we focus our attention on a negative sample selection method to properly balance the training data for cascade detectors. The method is based on the selection of the most informative false positive samples generated in one stage to feed the next stage. The results show that the proposed cascade detector with sample selection obtains on average better partial AUC and smaller standard deviation than the other compared cascade detectors.
Highlights
Viola and Jones proposed an efficient cascade framework that rapidly discards negatives and spends more time in positive candidates
In some detection problems such as face detection, medical lesion detection, or pedestrian detection the negative set typically contains orders of magnitude more images than the positive set. These training datasets are such that the positive set has too few samples and/ or the negative set should represent anything except the object of interest and this can give rise to biased classifiers
In this work a cascade detector with sample selection is proposed for improving cascade detectors
Summary
Viola and Jones proposed an efficient cascade framework that rapidly discards negatives and spends more time in positive candidates. Some authors have proposed modifications to the original cascade detector in order to improve the detection rate while maintaining or reducing the false positive rate (see Section). In some detection problems such as face detection, medical lesion detection, or pedestrian detection the negative set typically contains orders of magnitude more images than the positive set. These training datasets are such that the positive set has too few samples and/ or the negative set should represent anything except the object of interest and this can give rise to biased classifiers. If a stage’s decision is positive the sample proceeds to the stage Otherwise it is discarded without further processing (Fig 1). Cascade detectors operate with high accuracyand are currently used for several types of detection problems [10]
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