2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDSS-AVV.10
Paper Title ADVERSARIAL OPTIMIZATION SCHEME FOR ONLINE TRACKING MODEL ADAPTATION IN AUTONOMOUS SYSTEMS
Authors Iason Karakostas, Vasileios Mygdalis, Ioannis Pitas, Aristotle University of Thessaloniki, Greece
SessionSS-AVV: Special Session: Autonomous Vehicle Vision
LocationArea A
Session Time:Monday, 20 September, 13:30 - 15:00
Presentation Time:Monday, 20 September, 13:30 - 15:00
Presentation Poster
Topic Special Sessions: Autonomous Vehicle Vision
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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.