Multi-object detection and multi-object-tracking in diverse driving situations is the main
challenge in autonomous vehicles. Vehicle manufacturers and research organizations are …

Current state-of-the-art trackers rely only on a target appearance model in order to localize
the object in each frame. Such approaches are however prone to fail in case of eg fast …

The problem of arbitrary object tracking has traditionally been tackled by learning a model of
the object's appearance exclusively online, using as sole training data the video itself …

In this paper, we develop a new approach of spatially supervised recurrent convolutional
neural networks for visual object tracking. Our recurrent convolutional network exploits the …

This paper improves state-of-the-art visual object trackers that use online adaptation. Our
core contribution is an offline meta-learning-based method to adjust the initial deep …

During the recent years, correlation filters have shown dominant and spectacular results for
visual object tracking. The types of the features that are employed in this family of trackers …

Robust object tracking requires knowledge and understanding of the object being tracked:
its appearance, its motion, and how it changes over time. A tracker must be able to modify its …

In this paper we introduce a fully end-to-end approach for visual tracking in videos that
learns to predict the bounding box locations of a target object at every frame. An important …

Environmental sustainability research is dependent on accurate land cover information.
Even with the increased number of satellite systems and sensors acquiring data with …

In this paper, we propose a recurrent filter generation methods for visual tracking. We
directly feed the target's image patch to a recurrent neural network (RNN) to estimate an …