Detecting curved objects against cluttered backgrounds

Jan Prokaj


Abstract

Abstract: This work addresses the problem of curved object detection against cluttered backgrounds. New low-level and mid-level features are presented to function in these environments. The low-level features are fast to compute, thanks to an integral image approach. The mid-level features are built from low-level features, and are optimized for curved object detection. The usefulness of these features is tested by designing an object detection algorithm using these features. Object detection is accomplished by transforming the mid-level features into weak classifiers, which then produce a strong classifier using AdaBoost. The object detection algorithm is tested on the problem of detecting heads with shoulders. The resulting performance is encouraging.


Maintained by Dian Gong