In video surveillance as well as automotive applications, so-called fisheye cameras are often employed to capture a very wide angle of view. As such cameras depend on projections quite different from the classical perspective projection, the resulting fisheye image and video data correspondingly exhibits non-rectilinear image characteristics. Typical image and video processing algorithms, however, are not designed for these fisheye characteristics. To be able to develop and evaluate algorithms specifically adapted to fisheye images and videos, a corresponding test data set is therefore introduced in this paper. The first of those sequences were generated during the authors' own work on motion estimation for fish-eye videos and further sequences have gradually been added to create a more extensive collection. The data set now comprises synthetically generated fisheye sequences, ranging from simple patterns to more complex scenes, as well as fisheye video sequences captured with an actual fisheye camera. For the synthetic sequences, exact information on the lens employed is available, thus facilitating both verification and evaluation of any adapted algorithms. For the real-world sequences, we provide calibration data as well as the settings used during acquisition. The sequences are freely available via www.lms.lnt.de/fisheyedataset/.
翻译:在视频监视和汽车应用中,通常使用所谓的鱼眼照相机来捕捉非常宽广的视野。由于这种照相机依靠的预测与古典视角投影不同,因此所产生的鱼眼图像和视频数据相应地显示的是非线性图像特征。但是,典型的图像和视频处理算法并不是针对这些鱼眼特征设计的。为了能够开发和评价特别适合鱼眼图像和视频的算法,本文件引入了相应的测试数据集。这些序列中的第一组是在作者自己对鱼眼视频和进一步序列的运动估计工作中产生的,这些序列已逐步添加,以建立更加广泛的收集。现在的数据集包括合成产生的鱼眼序列,从简单的模式到更复杂的场景,以及用实际鱼眼摄影机摄取的鱼眼视频序列。对于合成序列,可以提供精确的镜头信息,从而便利对任何经过调整的算法进行核查和评价。对于现实世界序列,我们提供了校准数据,并提供了在获取过程中使用的设置。序列可以通过www.demem/fisherems自由提供。