@article {AbuAlhaija2018,
	title = {Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes},
	journal = {International Journal of Computer Vision},
	volume = {126},
	number = {9},
	year = {2018},
	month = {aug},
	pages = {961{\textendash}972},
	abstract = {The success of deep learning in computer vision is based on the availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Unfortunately, creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we propose an alternative paradigm which combines real and synthetic data for learning semantic instance segmentation and object detection models. Exploiting the fact that not all aspects of the scene are equally important for this task, we propose to augment real-world imagery with virtual objects of the target category. Capturing real-world images at large scale is easy and cheap, and directly provides real background appearances without the need for creating complex 3D models of the environment. We present an efficient procedure to augment these images with virtual objects. In contrast to modeling complete 3D environments, our data augmentation approach requires only a few user interactions in combination with 3D models of the target object category. Leveraging our approach, we introduce a novel dataset of augmented urban driving scenes with 360 degree images that are used as environment maps to create realistic lighting and reflections on rendered objects. We analyze the significance of realistic object placement by comparing manual placement by humans to automatic methods based on semantic scene analysis. This allows us to create composite images which exhibit both realistic background appearance as well as a large number of complex object arrangements. Through an extensive set of experiments, we conclude the right set of parameters to produce augmented data which can maximally enhance the performance of instance segmentation models. Further, we demonstrate the utility of the proposed approach on training standard deep models for semantic instance segmentation and object detection of cars in outdoor driving scenarios. We test the models trained on our augmented data on the KITTI 2015 dataset, which we have annotated with pixel-accurate ground truth, and on the Cityscapes dataset. Our experiments demonstrate that the models trained on augmented imagery generalize better than those trained on fully synthetic data or models trained on limited amounts of annotated real data.},
	keywords = {autonomous driving, Data augmentation, instance segmentation, Object detection, synthetic training data},
	issn = {15731405},
	doi = {10.1007/s11263-018-1070-x},
	url = {http://arxiv.org/abs/1708.01566},
	author = {Hassan Abu Alhaija and Mustikovela, Siva Karthik and Mescheder, Lars and Geiger, Andreas and Carsten Rother}
}
@conference {Mund2015a,
	title = {Performance evaluation of LiDAR point clouds towards automated FOD detection on airport aprons},
	booktitle = {Proceedings of ATACCS 2015 - 5th International Conference on Application and Theory of Automation in Command and Control Systems},
	year = {2015},
	pages = {85{\textendash}94},
	abstract = {Both the current system of airport ground control and the continuous implementation efforts of A-SMGCS and Remote Tower concepts require complete and independent surveillance coverage in real-time. We believe that 3D point clouds generated by an actively scanning LiDAR system available at TU Dresden may satisfy these high standards. Nonetheless, the utilization of LiDAR sensing for airport ground surveillance purposes is extremely challenging due to the unique requirement profile in this domain. This is also the reason why existing solutions in other domains such as autonomous driving and robotics are not directly applicable for airport ground surveillance. In a first step, we developed point cloud object detection and segmentation techniques to present that new data comprehensively to the airport apron controller. In this paper, we focused on the timely detection of dislocated objects (foreign object debris, forgotten equipment etc.) as a serious cause to hazardous situations on airport movement areas. The results are promising for various reference targets. However, the detection of very small objects (e.g. socket wrench) requires more elaborate algorithms to take full advantage of the current LiDAR technology. In the future we will assess the strength of LiDAR-based surveillance in terms of the number of hazardous situations that could be avoided or safely managed by the apron controller.},
	keywords = {Airport surveillance, apron control, Apron management service, FOD, Foreign object debris, laser scanning, LiDAR, Object detection, Point cloud},
	isbn = {9781450335621},
	doi = {10.1145/2899361.2899370},
	author = {Mund, Johannes and Zouhar, Alexander and Meyer, Lothar and Fricke, Hartmut and Carsten Rother}
}
@conference {Mund2015,
	title = {Performance evaluation of LiDAR point clouds towards automated FOD detection on airport aprons},
	booktitle = {Proceedings of ATACCS 2015 - 5th International Conference on Application and Theory of Automation in Command and Control Systems},
	year = {2015},
	pages = {85{\textendash}94},
	abstract = {Both the current system of airport ground control and the continuous implementation efforts of A-SMGCS and Remote Tower concepts require complete and independent surveillance coverage in real-time. We believe that 3D point clouds generated by an actively scanning LiDAR system available at TU Dresden may satisfy these high standards. Nonetheless, the utilization of LiDAR sensing for airport ground surveillance purposes is extremely challenging due to the unique requirement profile in this domain. This is also the reason why existing solutions in other domains such as autonomous driving and robotics are not directly applicable for airport ground surveillance. In a first step, we developed point cloud object detection and segmentation techniques to present that new data comprehensively to the airport apron controller. In this paper, we focused on the timely detection of dislocated objects (foreign object debris, forgotten equipment etc.) as a serious cause to hazardous situations on airport movement areas. The results are promising for various reference targets. However, the detection of very small objects (e.g. socket wrench) requires more elaborate algorithms to take full advantage of the current LiDAR technology. In the future we will assess the strength of LiDAR-based surveillance in terms of the number of hazardous situations that could be avoided or safely managed by the apron controller.},
	keywords = {Airport surveillance, apron control, Apron management service, FOD, Foreign object debris, laser scanning, LiDAR, Object detection, Point cloud},
	isbn = {9781450335621},
	doi = {10.1145/2899361.2899370},
	author = {Mund, Johannes and Zouhar, Alexander and Meyer, Lothar and Fricke, Hartmut and Carsten Rother}
}
@conference {Hoai2014,
	title = {Learning discriminative localization from weakly labeled data},
	booktitle = {Pattern Recognition},
	volume = {47},
	number = {3},
	year = {2014},
	month = {mar},
	pages = {1523{\textendash}1534},
	abstract = {Visual categorization problems, such as object classification or action recognition, are increasingly often approached using a detection strategy: a classifier function is first applied to candidate subwindows of the image or the video, and then the maximum classifier score is used for class decision. Traditionally, the subwindow classifiers are trained on a large collection of examples manually annotated with masks or bounding boxes. The reliance on time-consuming human labeling effectively limits the application of these methods to problems involving very few categories. Furthermore, the human selection of the masks introduces arbitrary biases (e.g., in terms of window size and location) which may be suboptimal for classification. We propose a novel method for learning a discriminative subwindow classifier from examples annotated with binary labels indicating the presence of an object or action of interest, but not its location. During training, our approach simultaneously localizes the instances of the positive class and learns a subwindow SVM to recognize them. We extend our method to classification of time series by presenting an algorithm that localizes the most discriminative set of temporal segments in the signal. We evaluate our approach on several datasets for object and action recognition and show that it achieves results similar and in many cases superior to those obtained with full supervision. {\textcopyright} 2013 Elsevier Ltd. All rights reserved.},
	keywords = {Discriminative discovery, Event detection, Image classification, Object detection, Time series classification, Weakly supervised learning},
	issn = {00313203},
	doi = {10.1016/j.patcog.2013.09.028},
	author = {Hoai, Minh and Torresani, Lorenzo and De La Torre, Fernando and Carsten Rother}
}
