Efficient Maximally Stable Extremal Region (MSER) Tracking

This paper introduces a tracking method for the well known local MSER (Maximally Stable Extremal Region) detector. The component tree is used as an efficient data structure, which allows the calculation of MSERs in quasi-linear time. It is demonstrated that the tree is able to manage the required data for tracking. We show that by means of MSER tracking the computational time for the detection of single MSERs can be improved by a factor of 4 to 10. Using a weighted feature vector for data association improves the tracking stability. Furthermore, the component tree enables backward tracking which further improves the robustness. The novel MSER tracking algorithm is evaluated on a variety of scenes. In addition, we demonstrate three different applications, tracking of license plates, faces and fibers in paper, showing in all three scenarios improved speed and stability.

1. Introduction

The detection of interest points and local features constitutes the basis for many important computer vision tasks. For example, object recognition, stereo matching, mosaicking, object tracking, indexing and database retrieval, robot navigation etc. rely on the detection of interest points which possess some distinguishing, highly invariant and stable properties. Such structures are often called distinguished regions (DR) [1] and provide a compact and abstract representation of patterns in an image.