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Related Experiment Videos

Mapping from frame-driven to frame-free event-driven vision systems by low-rate rate coding and coincidence

José Antonio Pérez-Carrasco¹, Bo Zhao, Carmen Serrano

¹University of Sevilla, Spain.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|September 21, 2013

Summary

This study introduces a method to convert frame-driven neural networks into event-driven systems for dynamic vision sensors (DVS). This enables faster object recognition using event-based processing, inspired by biological vision.

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Area of Science:

Computer Vision
Neuroscience
Robotics

Background:

Conventional video systems use frame rates, limiting real-time processing.
Event-driven sensors mimic biological systems, processing visual changes asynchronously.
Dynamic Vision Sensors (DVS) detect temporal contrast, offering microsecond-level event data.

Purpose of the Study:

To present a methodology for mapping frame-driven neural networks to event-driven representations.
To enable real-time object recognition using event-driven convolutional neural networks (ConvNets).

Main Methods:

Developed a method to translate trained frame-driven neural networks into an event-driven format.
Utilized event-driven ConvNets trained on rotating human silhouettes and poker card symbols.
Employed a dedicated event-driven simulator and real DVS camera recordings for validation.

Main Results:

Demonstrated successful mapping of conventional neural networks to event-driven architectures.
Achieved near-coincident input and output event flows for rapid processing.
Enabled object recognition with minimal delay using event-driven processing.

Conclusions:

The proposed methodology facilitates the use of event-driven vision sensors for high-speed object recognition.
Event-driven ConvNets offer a promising approach for real-time visual processing tasks.
This research bridges conventional deep learning with bio-inspired event-based sensing.