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

Authors :
Tapiador-Morales R
Maro JM
Jimenez-Fernandez A
Jimenez-Moreno G
Benosman R
Linares-Barranco A
Source :
Sensors (Basel, Switzerland) [Sensors (Basel)] 2020 Jun 16; Vol. 20 (12). Date of Electronic Publication: 2020 Jun 16.
Publication Year :
2020

Abstract

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.

Details

Language :
English
ISSN :
1424-8220
Volume :
20
Issue :
12
Database :
MEDLINE
Journal :
Sensors (Basel, Switzerland)
Publication Type :
Academic Journal
Accession number :
32560238
Full Text :
https://doi.org/10.3390/s20123404