False Positive Reduction in Detector Implantation
The development of a detection system is normally driven to achieve good detection rates. In most cases, a good detection rate involves a number of false positive decisions. However, the false positive rate is ultimately what decides if the detection system is effective or not. Another aspect to consider in automatic detection systems is the time to analyse an image until a decision is made. Viola & Jones proposed a cascade detector that achieves good detection and false positive rates at high speed. Some authors have proposed modifications to the cascade detector in order to improve the detection rate while maintaining the same false positive rate. However, during the implantation of the system we consistently find a large number of false positive detections due to the lack of knowledge about the newly acquired images. In this work, we propose a parallel cascade detector that gradually incorporates these new false positives to achieve an acceptable false positive rate. The second cascade detector is built using the new false positive detection images and the original true positive images during the implantation period. The proposed parallel scheme reduces the false positive rate of the system at roughly the same speed.
KeywordsFalse Positive Reduction Automatic Detection Cascade of Classifiers
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