Large-scale public datasets are vital for algorithm development in the computer vision field. Thanks to the availability of advanced sensors such as cameras, Lidar and Kinect, massive well-designed datasets created by researchers are free to the scientific and academic world. ImageNet (Deng et al., 2009) is one of the most representative examples which is widely used for image recognition tasks in computer vision. UCF 101 (Soomro et al., 2012) is another large-scale dataset used for human action recognition. However, both of the above datasets provide only the appearance information of objects in the scene. With the limited information provided by RGB images, it is extremely difficult to solve certain problems such as the partition of the foreground and background which have similar colors and textures. With the release of the low-cost Kinect sensor in 2010, acquisition of RGB and depth data became cheaper and easier. Not surprisingly, increasing RGB-D datasets, recorded by the Kinect sensor and dedicated to a wide range of applications, have become available (Cai et al., 2017). We see the same trend, the KITTI dataset (Geiger et al., 2013), starting to occur in the autonomous driving community due to the availability of the Velodyne HDL-64E rotating 3D laser scanner. It is clear that the advent of new sensors always brings opportunities for new dataset development. In this data report, we introduce three new neuromorphic vision datasets recorded by a novel neuromorphic vision sensor named Dynamic Vision Sensors (DVS) (Lichtsteiner et al., 2008). DVS is a novel type of neuromorphic-based vision sensor, developed by Lichtsteiner et al. (2008). The sensor records event streams as a sequence of tuples ...
Language: en
", language="en", issn="1662-5218", doi="10.3389/fnbot.2019.00038", url="http://dx.doi.org/10.3389/fnbot.2019.00038" }