Mapping from Frame-Driven to Frame-Free Event-Driven Vision Systems by Low-Rate Rate-Coding and Coincidence Processing. Application to Feed-Forward ConvNets
Event-driven visual sensors have attracted interest from a number of different research communities. They provide visual information in quite a different way from conventional video systems consisting of sequences of still images rendered at a given “frame rate”. Event-driven vision sensors take ins...
| Autores: | , , , , , , |
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| Formato: | artículo |
| Estado: | Versión enviada para evaluación y publicación |
| Fecha de publicación: | 2013 |
| País: | España |
| Recursos: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/79657 |
| Acesso em linha: | https://hdl.handle.net/11441/79657 https://doi.org/10.1109/TPAMI.2013.71 |
| Access Level: | acceso abierto |
| Palavra-chave: | Feature Extraction Convolutional Neural Networks Object Recognition |
| Resumo: | Event-driven visual sensors have attracted interest from a number of different research communities. They provide visual information in quite a different way from conventional video systems consisting of sequences of still images rendered at a given “frame rate”. Event-driven vision sensors take inspiration from biology. Each pixel sends out an event (spike) when it senses something meaningful is happening, without any notion of a frame. A special type of Event-driven sensor is the so called Dynamic-Vision-Sensor (DVS) where each pixel computes relative changes of light, or “temporal contrast”. The sensor output consists of a continuous flow of pixel events which represent the moving objects in the scene. Pixel events become available with micro second delays with respect to “reality”. These events can be processed “as they flow” by a cascade of event (convolution) processors. As a result, input and output event flows are practically coincident in time, and objects can be recognized as soon as the sensor provides enough meaningful events. In this paper we present a methodology for mapping from a properly trained neural network in a conventional Frame-driven representation, to an Event-driven representation. The method is illustrated by studying Event-driven Convolutional Neural Networks (ConvNet) trained to recognize rotating human silhouettes or high speed poker card symbols. The Event-driven ConvNet is fed with recordings obtained from a real DVS camera. The Event-driven ConvNet is simulated with a dedicated Event-driven simulator, and consists of a number of Event-driven processing modules the characteristics of which are obtained from individually manufactured hardware modules. |
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