Pedestrian Detection with Minimal False Positives per Color-Thermal Image

This research is based on aggregate channel features utilized for pedestrian detection, and the main focus is to investigate a simple way to reduce the number of false positives per image. The importance of this will be to increase the accuracy of the detector by removing the excessive number of false positives while maintaining the missing rate as low as possible. To omit such unwanted false positives, we utilized an image categorization method for day and night images in order to minimize the misclassification rate. Furthermore, the best extension of the aggregate channel features method was analyzed and is recommended as a base detector. As a result, a night-time pre-trained pedestrian detector is only applied to night images, and a daytime detector is applied to daytime images. Thus, a large number of false positives are avoided while the missing rate is greatly reduced.