Fuzzy component based object detection

Abstract

Component based object detection approaches have been shown to significantly improve object detection performance in adversities such as occlusion, variations in pose, in and out of plane rotation and poor illumination. Even the best object detectors are prone to errors when used in a global object detection scheme (one that uses the whole object as a single entity for detection purpose), due to these problems. We propose a fuzzy approach to object detection that treats an object as a set of constituent components rather than a single entity. The object detection task is completed in two steps. In the first step, candidates for respective components are selected based on their appearance match and handed over to the geometrical configuration classifier. The geometrical configuration classifier is a fuzzy inference engine that selects one candidate for each component such that each candidate is a reasonable match to the corresponding component in terms of appearance and also a good fit for the overall geometrical model. The detected object consists of candidates that are not necessarily the best in terms of appearance match or the closest to the geometrical model in terms of placement. The output is a set of candidates that is an optimal combination satisfying both criteria. We evaluate the technique on a well known face dataset and show that the technique results in detection of most faces in a scale-invariant manner. The technique has been shown to be robust to in-plane rotations and occlusion.