Fast Visual Object Tracking Using Convolutional Filters

Recently, a class of tracking techniques called synthetic exact filters has been shown to give promising results at impressive speeds. Synthetic exact filters are trained using a large number of training images and associated continuous labels, however, there is not much theory behind it. In this paper, we theoretically explain the reason why synthetic exact filters based methods work well and propose a novel visual object tracking algorithm based on convolutional filters, which are trained only by training images without labels. Compared with the prior methods such as synthetic exact filters which are trained by training images and labels, advantages of the convolutional filters training include: faster and more robust than synthetic exact filters, insensitive to parameters and simpler in pre-processing of training images. Convolutional filters are theoretically optimal in terms of the signal-to-noise ratio. Furthermore, we utilize spatial context information to improve robustness of our tracking system. Experiments on many challenging video sequences demonstrate that our convolutional filters based tracker is competitive with the state-of-the-art trackers in accuracy and outperforms most trackers in efficiency.