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| Content Provider | frontiers |
|---|---|
| Author | Hu, Yuhuang Liu, Hongjie Pfeiffer, Michael Delbruck, Tobi |
| Abstract | INTRODUCTIONBenchmarks have played a vital role in the advancement of visual object recognition and other fields ofcomputer vision (LeCun et al., 1998; Krizhevsky and Hinton, 2009; Deng et al., 2009). The challenges posedby these standard datasets have helped identify and overcome the shortcomings of existing approaches,and have led to great advances of the state of the art. Even the recent massive increase of interest in deeplearning methods can be attributed to their success in difficult benchmarks such as ImageNet (Krizhevskyet al., 2012; LeCun et al., 2015). Neuromorphic vision uses silicon retina sensors such as the dynamicvision sensor (DVS) (Lichtsteiner et al., 2008). These sensors and their DAVIS (Dynamic and Active-pixelVision Sensor) and ATIS (Asynchronous Time-based Image Sensor) derivatives (Brandli et al., 2014; Poschet al., 2014) are inspired by biological vision by generating streams of asynchronous events indicating locallog-intensity brightness changes. They thereby greatly reduce the amount of data to be processed, and theirdynamic nature makes them a good fit for domains such as optical flow, object tracking, action recognition,or dynamic scene understanding. Compared to classical computer vision, neuromorphic vision is a youngerand much smaller field of research, and lacks benchmarks, which impedes the progress of the field. Toaddress this we introduce the largest event-based vision benchmark dataset published to date, hoping tosatisfy a growing dema... |
| ISSN | 1662453X |
| DOI | 10.3389/fnins.2016.00405 |
| Volume Number | 10 |
| Journal | Frontiers in Neuroscience |
| Language | English |
| Publisher Date | 2016-08-31 |
| Access Restriction | Open |
| Subject Keyword | AER Object Tracking Neuromorphic Action recognition Benchmarks Object recognition Event-based vision DVS |
| Content Type | Text |
| Resource Type | Article |
| Subject | Neuroscience |
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