scholarly journals Low Latency Event-Based Filtering and Feature Extraction for Dynamic Vision Sensors in Real-Time FPGA Applications

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 134926-134942 ◽  
Author(s):  
Alejandro Linares-Barranco ◽  
Fernando Perez-Pena ◽  
Diederik Paul Moeys ◽  
Francisco Gomez-Rodriguez ◽  
Gabriel Jimenez-Moreno ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Low Latency ◽  
Vision Sensors ◽  
Dynamic Vision ◽  
Event Based

Event-based Row-by-Row Multi-convolution engine for Dynamic-Vision Feature Extraction on FPGA

2018 ◽  
Author(s):  
Ricardo Tapiador-Morales ◽  
Antonio Rios-Navarro ◽  
Juan P. Dominguez-Morales ◽  
D. Gutierrez-Galan ◽  
M. Dominguez-Morales ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Dynamic Vision ◽  
Event Based

A USB3.0 FPGA event-based filtering and tracking framework for dynamic vision sensors

2015 ◽  
Author(s):  
A. Linares-Barranco ◽  
F. Gomez-Rodriguez ◽  
V. Villanueva ◽  
L. Longinotti ◽  
T. Delbruck
Keyword(s):  
Vision Sensors ◽  
Dynamic Vision ◽  
Event Based

scholarly journals Event-Based Gesture Recognition through a Hierarchy of Time-Surfaces for FPGA

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3404 ◽  
Author(s):  
Ricardo Tapiador-Morales ◽  
Jean-Matthieu Maro ◽  
Angel Jimenez-Fernandez ◽  
Gabriel Jimenez-Moreno ◽  
Ryad Benosman ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Time History ◽  
High Temporal Resolution ◽  
Vision Sensors ◽  
Dynamic Vision ◽  
Mammalian Retina ◽  
Continuous Stream ◽  
Spatio Temporal ◽  
Event Based ◽  
Embedded Applications

Neuromorphic vision sensors detect changes in luminosity taking inspiration from mammalian retina and providing a stream of events with high temporal resolution, also known as Dynamic Vision Sensors (DVS). This continuous stream of events can be used to extract spatio-temporal patterns from a scene. A time-surface represents a spatio-temporal context for a given spatial radius around an incoming event from a sensor at a specific time history. Time-surfaces can be organized in a hierarchical way to extract features from input events using the Hierarchy Of Time-Surfaces algorithm, hereinafter HOTS. HOTS can be organized in consecutive layers to extract combination of features in a similar way as some deep-learning algorithms do. This work introduces a novel FPGA architecture for accelerating HOTS network. This architecture is mainly based on block-RAM memory and the non-restoring square root algorithm, requiring basic components and enabling it for low-power low-latency embedded applications. The presented architecture has been tested on a Zynq 7100 platform at 100 MHz. The results show that the latencies are in the range of 1 μ s to 6.7 μ s, requiring a maximum dynamic power consumption of 77 mW. This system was tested with a gesture recognition dataset, obtaining an accuracy loss for 16-bit precision of only 1.2% with respect to the original software HOTS.

scholarly journals A Saccade Based Framework for Real-Time Motion Segmentation Using Event Based Vision Sensors

2017 ◽  
Vol 11 ◽  
Author(s):  
Abhishek Mishra ◽  
Rohan Ghosh ◽  
Jose C. Principe ◽  
Nitish V. Thakor ◽  
Sunil L. Kukreja
Keyword(s):  
Real Time ◽  
Motion Segmentation ◽  
Time Motion ◽  
Vision Sensors ◽  
Event Based

scholarly journals Toward real-time particle tracking using an event-based dynamic vision sensor

2011 ◽  
Vol 51 (5) ◽  
pp. 1465-1469 ◽  
Author(s):  
David Drazen ◽  
Patrick Lichtsteiner ◽  
Philipp Häfliger ◽  
Tobi Delbrück ◽  
Atle Jensen
Keyword(s):  
Real Time ◽  
Particle Tracking ◽  
Vision Sensor ◽  
Dynamic Vision ◽  
Event Based
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