Adversarial Optimization Scheme For Online Tracking Model Adaptation In Autonomous Systems

Online tracking model updating is typically addressed as a regression problem, involving the minimization of the dispersion between the obtained tracker model response maps in each consecutive frame and some target distribution (e.g., Gaussian), using a closed-form solution. Inspired by the recent applications of Generative Adversarial Networks (GANs), we propose to solve this problem with an adversarial optimization scheme, by employing a Generator-Discriminator network pair. That is, the role of the Generator is assigned to the tracking model so that it produces response maps belonging to some target distribution, while an additional discriminator network is trained to identify if the tracker response maps produced by the generator belong to this target distribution, or not. Therefore, the tracker model exploits the discriminator network as an additional information pool about the target distribution. It is shown that this simple addition improves tracking performance in standard benchmark datasets, without significantly hurting training complexity, thus rendering the proposed method suitable for embedded system application such as in autonomous cars and Unmanned Aerial Systems.