Serrano-Gotarredona Teresa, Linares-Barranco Bernabé
Instituto de Microelectrónica de Sevilla (IMSE-CNM), CSIC and Universidad de Sevilla Sevilla, Spain.
Front Neurosci. 2015 Dec 22;9:481. doi: 10.3389/fnins.2015.00481. eCollection 2015.
This article reports on two databases for event-driven object recognition using a Dynamic Vision Sensor (DVS). The first, which we call Poker-DVS and is being released together with this article, was obtained by browsing specially made poker card decks in front of a DVS camera for 2-4 s. Each card appeared on the screen for about 20-30 ms. The poker pips were tracked and isolated off-line to constitute the 131-recording Poker-DVS database. The second database, which we call MNIST-DVS and which was released in December 2013, consists of a set of 30,000 DVS camera recordings obtained by displaying 10,000 moving symbols from the standard MNIST 70,000-picture database on an LCD monitor for about 2-3 s each. Each of the 10,000 symbols was displayed at three different scales, so that event-driven object recognition algorithms could easily be tested for different object sizes. This article tells the story behind both databases, covering, among other aspects, details of how they work and the reasons for their creation. We provide not only the databases with corresponding scripts, but also the scripts and data used to generate the figures shown in this article (as Supplementary Material).
本文报道了两个用于使用动态视觉传感器(DVS)进行事件驱动目标识别的数据库。第一个数据库,我们称之为扑克-DVS,与本文一同发布,它是通过在DVS相机前浏览特制的扑克牌组2至4秒获得的。每张牌在屏幕上出现约20至30毫秒。扑克点数经过离线跟踪和分离,构成了包含131条记录的扑克-DVS数据库。第二个数据库,我们称之为MNIST-DVS,于2013年12月发布,它由一组30000条DVS相机记录组成,这些记录是通过在液晶显示器上每次显示约2至3秒来自标准MNIST 70000图像数据库中的10000个移动符号获得的。10000个符号中的每一个都以三种不同比例显示,以便可以轻松测试事件驱动目标识别算法对不同物体大小的性能。本文讲述了这两个数据库背后的故事,涵盖了它们的工作方式细节以及创建原因等多个方面。我们不仅提供了带有相应脚本的数据库,还提供了用于生成本文所示图形的脚本和数据(作为补充材料)。