Accurate and robust tracking of rigid objects in real time

We present the shape model object tracker, which is accurate, robust, and real-time capable on a standard CPU. The tracker has a failure mode detection, is robust to nonlinear illumination changes, and can cope with occlusions. It uses subpixel-precise image edges to track roughly rigid objects with high accuracy and is virtually drift-free even for long sequences. Furthermore, it is inherently capable of object re-detection when tracking fails. To evaluate the accuracy, robustness, and efficiency of the tracker precisely, we present a challenging new tracking dataset with pixel-precise ground truth. The precise ground-truth labels are created automatically from the photo-realistic synthetic VIPER dataset. The tracker is thoroughly evaluated against the state of the art through a number of qualitative and quantitative experiments. It is able to perform on par with the current state-of-the-art deep-learning trackers, but is at least 45 times faster, even without using a GPU. The efficiency and low memory consumption of the tracker are validated in further experiments that are conducted on an embedded device.

Automated summary

This study introduces an accurate, robust, and real-time shape model object tracker with failure mode detection and robustness to nonlinear illumination changes. Through experiments, the tracker performs well, with fast speed and low memory consumption.