English

Abstract

Detection of articulated objects in images, including location and state, is an important and challenging task in many object tracking applications. Image edges have proven to be a key feature, although their quality is influenced by many factors. In this paper, we propose a novel edge gradient-based template matching method for object detection. In contrast to other methods, ours does not perform any binarization or discretization during the online matching. This is facilitated by a new continuous edge gradient similarity measure. Its main components are a novel edge gradient operator, which is applied to query and template images, and the formulation as a convolution, which can be computed very efficiently in Fourier space. Our method consists of a preprocessing stage for the template images, a simple preprocessing of the query image, and our similarity measure between template and query image, which yields a confidence map for the query image. The resulting confidence map can be used for the final object localization. We compared our method to a state-of-the-art chamfer-based matching method. The results demonstrate that our method is much more robust against weak edge response and yields much better confidence maps with fewer maxima that are also more significant. In addition, our method lends itself well to efficient implementation on GPUs: at a query image resolution of 320× 256 and a template resolution of 80× 80 we can generate about 330 confidence maps per second.