Specific Object Detection Scheme Based on Descriptors Fusion Using Belief Functions

Here, a comparative study of information fusion methods for instance object detection is proposed. Instance object detection is one of mean service that robots needs. Classical approaches are based on extracting discriminant and invariant features. However those features still have a limitation to represent all kinds of objects and satisfy all requirements (discrimination and invariance). Since no single feature can work well in various situations, we need to combine several features so that the robot can handle all kind of daily life objects. Our task consists in defining a strategy that can work on various objects and backgrounds without any prior knowledge. In this paper we propose a scheme to combine two descriptors using belief function theory. First, objects are extracted from image and described by two complementary descriptors: Dominant Color Descriptor for color description and Zernike Moments for shape description. Second, similarity indicators is computed between object of interest descriptors and each extracted object descriptors. Finally, those measures are combined into a belief functions in order to build a final decision about the object presence in the image taking the information uncertainty and imprecise into consideration. We have evaluated our approach with different methods of information fusion such as the weighted vote approach, the possibility theory and so forth.