Rapid selection of reliable templates for visual tracking

We propose a method that rates the suitability of given templates for template-based tracking in real-time. This is important for applications with online template selection, such as SLAM, where it is essential to track a low number of preferably reliable templates. Our approach is based on simple image features specifically designed to identify texture properties which are problematic for tracking. During a training step, a support vector régresser is learned. It uses a tracking quality measure which considers both convergence rate and speed obtained by simulation of many tracking attempts. Finally, a minimum set of image features is identified to speedup the online selection process. In experiments on real-world video sequences our method improved the detection rate of an existing tracking-by-detection system by 8% on average.