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CIFAR10-DVS: An Event-Stream Dataset for Object Classification
- Source :
- Frontiers in Neuroscience, Vol 11 (2017)
- Publication Year :
- 2017
- Publisher :
- Frontiers Media S.A., 2017.
-
Abstract
- Neuromorphic vision research requires high-quality and appropriately challenging event-stream datasets to support continuous improvement of algorithms and methods. However, creating event-stream datasets is a time-consuming task, which needs to be recorded using the neuromorphic cameras. Currently, there are limited event-stream datasets available. In this work, by utilizing the popular computer vision dataset CIFAR-10, we converted 10,000 frame-based images into 10,000 event streams using a dynamic vision sensor (DVS), providing an event-stream dataset of intermediate difficulty in 10 different classes, named as “CIFAR10-DVS.” The conversion of event-stream dataset was implemented by a repeated closed-loop smooth (RCLS) movement of frame-based images. Unlike the conversion of frame-based images by moving the camera, the image movement is more realistic in respect of its practical applications. The repeated closed-loop image movement generates rich local intensity changes in continuous time which are quantized by each pixel of the DVS camera to generate events. Furthermore, a performance benchmark in event-driven object classification is provided based on state-of-the-art classification algorithms. This work provides a large event-stream dataset and an initial benchmark for comparison, which may boost algorithm developments in even-driven pattern recognition and object classification.
Details
- Language :
- English
- ISSN :
- 1662453X
- Volume :
- 11
- Database :
- Directory of Open Access Journals
- Journal :
- Frontiers in Neuroscience
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.7193fd5d3a6b4f199fed9bc9e016e7fb
- Document Type :
- article
- Full Text :
- https://doi.org/10.3389/fnins.2017.00309