Next Article in Journal
Defense Strategies for Asymmetric Networked Systems with Discrete Components
Next Article in Special Issue
Promoting Pollution-Free Routes in Smart Cities Using Air Quality Sensor Networks
Previous Article in Journal
Distributed Fast Self-Organized Maps for Massive Spectrophotometric Data Analysis
Previous Article in Special Issue
The Use of Drones in Spain: Towards a Platform for Controlling UAVs in Urban Environments
Open AccessArticle

A Finite State Machine Approach to Algorithmic Lateral Inhibition for Real-Time Motion Detection

Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, 02071-Albacete, Spain
*
Author to whom correspondence should be addressed.
Homage to the memory of Prof José Mira, our close master and friend, 10 years after his death.
Sensors 2018, 18(5), 1420; https://doi.org/10.3390/s18051420
Received: 11 April 2018 / Revised: 30 April 2018 / Accepted: 1 May 2018 / Published: 3 May 2018
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2018)
Many researchers have explored the relationship between recurrent neural networks and finite state machines. Finite state machines constitute the best-characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. The neurally-inspired lateral inhibition method, and its application to motion detection tasks, have been successfully implemented in recent years. In this paper, control knowledge of the algorithmic lateral inhibition (ALI) method is described and applied by means of finite state machines, in which the state space is constituted from the set of distinguishable cases of accumulated charge in a local memory. The article describes an ALI implementation for a motion detection task. For the implementation, we have chosen to use one of the members of the 16-nm Kintex UltraScale+ family of Xilinx FPGAs. FPGAs provide the necessary accuracy, resolution, and precision to run neural algorithms alongside current sensor technologies. The results offered in this paper demonstrate that this implementation provides accurate object tracking performance on several datasets, obtaining a high F-score value (0.86) for the most complex sequence used. Moreover, it outperforms implementations of a complete ALI algorithm and a simplified version of the ALI algorithm—named “accumulative computation”—which was run about ten years ago, now reaching real-time processing times that were simply not achievable at that time for ALI. View Full-Text
Keywords: formal model; finite state machines; artificial neural networks; motion detection; field programmable gate array formal model; finite state machines; artificial neural networks; motion detection; field programmable gate array
Show Figures

Figure 1

MDPI and ACS Style

López, M.T.; Bermúdez, A.; Montero, F.; Sánchez, J.L.; Fernández-Caballero, A. A Finite State Machine Approach to Algorithmic Lateral Inhibition for Real-Time Motion Detection . Sensors 2018, 18, 1420.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop