Shape Guided Maximally Stable Extremal Region (MSER) Tracking

Maximally Stable Extremal Regions (MSERs) are one of the most prominent interest region detectors in computer vision due to their powerful properties and low computational demands. In general MSERs are detected in single images, but given image sequences as input, the repeatability of MSER detection can be improved by exploiting correspondences between subsequent frames by feature based analysis. Such an approach fails during fast movements, in heavily cluttered scenes and in images containing several similar sized regions because of the simple feature based analysis. In this paper we propose an extension of MSER tracking by considering shape similarity as strong cue for defining the frame-to-frame correspondences. Efficient calculation of shape similarity scores ensures that real-time capability is maintained. Experimental evaluation demonstrates improved repeatability and an application for tracking weakly textured, planar objects.