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In this paper, we develop a photo-realistic simulator that can afford the generation of large amounts of training data (both images rendered from the UAV camera and its controls) to teach a UAV to autonomously race through challenging tracks. We train a deep neural network to predict UAV controls from raw image data for the task of autonomous UAV racing. Training is done through imitation learning enabled by data augmentation to allow for the correction of navigation mistakes. Extensive experiments demonstrate that our trained network (when sufficient data augmentation is used) outperforms state-of-the-art methods and flies more consistently than many human pilots.
arXiv’17

We present a photo-realistic training and evaluation simulator (UE4Sim) with extensive applications across various fields of computer vision. Built on top of the Unreal Engine, the simulator integrates full featured physics based cars, unmanned aerial vehicles (UAVs), and animated human actors in diverse urban and suburban 3D environments. We demonstrate the versatility of the simulator with two case studies: autonomous UAV-based tracking of moving objects and autonomous driving using supervised learning. The simulator fully integrates both several state-of-the-art tracking algorithms with a benchmark evaluation tool and a deep neural network (DNN) architecture for training vehicles to drive autonomously. It generates synthetic photo-realistic datasets with automatic ground truth annotations to easily extend existing real-world datasets and provides extensive synthetic data variety through its ability to reconfigure synthetic worlds on the fly using an automatic world generation tool.
arXiv’17

In this paper, we present a framework that allows the explicit incorporation of global context within CF trackers. We reformulate the original optimization problem and provide a closed form solution for single and multi-dimensional features in the primal and dual domain. We demonstrate with extensive experiments that this framework can significantly improve the performance of many CF trackers with only a modest impact on their frame rate.​
Oral at CVPR’17

In order to evaluate object trackers for aerial tracking applications, we have generated 123 new video sequences from a UAV and annotated them with upright bounding boxes and attributes relevant for tracking. With more than 110,000 frames this is currently the largest data set for aerial tracking by a long shot and the second largest data set for generic tracking. In addition, we have developed a photo-realistic simulator within the UE4 framework that can be used for numerous vision tasks. As an example, we have integrated several state-of-the-art object trackers to control an UAV inside the simulator based on live feedback. In addition our simulator can be used to generate large amounts of realistic vision data.
Poster at ECCV’16

We have developed a persistent, robust and autonomous object tracking system for unmanned aerial vehicles (UAVs). Our computer vision and control strategy integrates multiple UAVs with a stabilized RGB camera and can be applied to a diverse set of moving objects (e.g. humans, animals, cars, boats, etc.). A novel strategy is employed to successfully track objects over a long period, by ’handing over the camera’ from one UAV to another. The popular object tracker Struck was optimized for both speed and performance and integrated into the proposed system.
Interactive Presentation at IROS’16

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