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.

我们介绍了形状模型对象跟踪器，它在标准CPU上是准确，可靠且实时的。跟踪器具有故障模式检测功能，对非线性照明变化具有鲁棒性，并且可以应对遮挡。它使用亚像素精确的图像边缘来高精度地跟踪大致的刚性物体，即使对于长序列也几乎没有漂移。此外，它固有地能够在跟踪失败时重新检测对象。为了精确评估跟踪器的准确性，鲁棒性和效率，我们提出了一个具有像素精度的地面真实性的具有挑战性的新跟踪数据集。精确的地面标签是根据逼真的合成VIPER数据集自动创建的。通过大量的定性和定量实验，对跟踪器进行了全面的评估，以确保其技术水平。它能够与当前最新的深度学习跟踪器相提并论，但即使不使用GPU，也至少快45倍。跟踪器的效率和低内存消耗在嵌入式设备上进行的其他实验中得到了验证。