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Regular version of the site

Book chapter

End-to-end Deep Object Tracking with Circular Loss Function for Rotated Bounding Box

P. 165-170.
Shpilman A., Malysheva A., Belyaev V.

The task object tracking is vital in numerous applications such as autonomous driving, intelligent surveillance, robotics, etc. This task entails the assigning of a bounding box to an object in a video stream, given only the bounding box for that object on the first frame. In 2015, a new type of video object tracking (VOT) dataset was created that introduced rotated bounding boxes as an extension of axis-aligned ones. In this work, we introduce a novel end-To-end deep learning method based on the Transformer Multi-Head Attention architecture. We also present a new type of loss function, which takes into account the bounding box overlap and orientation. Our Deep Object Tracking model with Circular Loss Function (DOTCL) shows a considerable improvement in terms of robustness over current state-of-The-Art end-To-end deep learning models. It also outperforms state-of-The-Art object tracking methods on VOT2018 dataset in terms of the expected average overlap (EAO) metric.