How Fusion of Multiple Views Can Improve Object Recognition in Real-World Environments

In the past decades most object recognition systems were based on passive approaches. But in the last few years a lot of research was done in the field of active object recognition. In this context there are several unique problems to be solved. One of them is how to fuse a series of images that might differ in their viewpoints. In this paper we present a well-founded approach for the fusion of multiple views based on a recursive density propagation method. It uses particle filters for solving the fusion in a continuous pose space. Furthermore we will show by means of a statistical object recognition system how to integrate such systems into our fusion approach. The experimental result will show, how the fusion can improve classification rates substantial, especially for difficult conditions like heterogeneous background within real world environments.