Figure 1: We introduce WoodScape, the first fisheye image dataset dedicated to autonomous driving. It contains four cameras covering 360°accompanied by a HD laser scanner, IMU and GNSS. Annotations are made available for nine tasks, notably 3D object detection, depth estimation (overlaid on front camera) and semantic segmentation as illustrated here.
AbstractFisheye cameras are commonly employed for obtaining a large field of view in surveillance, augmented reality and in particular automotive applications. In spite of its prevalence, there are few public datasets for detailed evaluation of computer vision algorithms on fisheye images. We release the first extensive fisheye automotive dataset, Wood-Scape, named after Robert Wood who invented the fisheye camera in 1906. WoodScape comprises of four surround view cameras and nine tasks including segmentation, depth estimation, 3D bounding box detection and soiling detection. Semantic annotation of 40 classes at the instance level is provided for over 10,000 images and annotation for other tasks are provided for over 100,000 images. We would like to encourage the community to adapt computer vision models for fisheye camera instead of naïve rectification. 1
We propose a principled approach to supervised learning of facial landmarks detector based on the Deformable Part Models (DPM). We treat the task of landmarks detection as an instance of the structured output classification. To learn the parameters of the detector we use the Structured Output Support Vector Machines algorithm. The objective function of the learning algorithm is directly related to the performance of the detector and controlled by the userdefined loss function, in contrast to the previous works. Our proposed detector is real-time on a standard computer, simple to implement and easily modifiable for detection of various set of landmarks. We evaluate the performance of our detector on a challenging "Labeled Faces in the Wild" (LFW) database. The empirical results show that our detector consistently outperforms two public domain implementations based on the Active Appearance Models and the DPM. We are releasing open-source code implementing our proposed detector along with the manual annotation of seven facial landmarks for nearly all images in the LFW database.1 There also exists a successful commercial solution OKAO Vision Facial Feature Extraction API (http://www.omron.com) which is used for example in Picasa TM or Apple iPhoto TM software.
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