Enhanced multiple-instance pruning for learning soft cascade detectors
Daniil P. Matalov, Vladimir V. Arlazarov · 2024
Object detection is one of the most common problems solved by computer vision systems. Even though neural network methods have become a standard tool for solving the problems, these methods have many disadvantages, which include high computational power requirements both for training and inference stages and tremendous training sets. This paper considers such a classical method for object detection as the Viola and Jones method and proposes an enhanced soft cascade calibration method based on Multiple-Instance Pruning to increase detection performance. The proposed method considers a response of the classifier to an image region as a random variable and follows a statistical approach to provide robust detectors. In addition, the paper addresses the problem of non-conformity of detection parameters at training and inference stages and studies performance decline. The performance of the proposed methods is demonstrated in a variety of practical tasks, including identity document detection and document fraud detection.