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LDSI - Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm

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LDSI - Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm

dc.contributor.author	Barrios Avilés, Juan
dc.contributor.author	Rosado Muñoz, Alfredo
dc.contributor.author	Medus, Leandro Daniel
dc.contributor.author	Bataller Mompean, Manuel
dc.contributor.author	Guerrero Martínez, Juan Francisco
dc.date.accessioned	2018-12-14T15:08:26Z
dc.date.available	2018-12-14T15:08:26Z
dc.date.issued	2018
dc.identifier.citation	Barrios Avilés, Juan Rosado Muñoz, Alfredo Medus, Leandro Daniel Bataller Mompean, Manuel Guerrero Martínez, Juan Francisco 2018 LDSI - Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm Sensors 18 12 4122
dc.identifier.uri	http://hdl.handle.net/10550/68272
dc.description.abstract	Sensors provide data which need to be processed after acquisition to remove noise and extract relevant information. When the sensor is a network node and acquired data are to be transmitted to other nodes (e.g., through Ethernet), the amount of generated data from multiple nodes can overload the communication channel. The reduction of generated data implies the possibility of lower hardware requirements and less power consumption for the hardware devices. This work proposes a filtering algorithm (LDSI - Less Data Same Information) which reduces the generated data from event-based sensors without loss of relevant information. It is a bioinspired filter, i.e., event data are processed using a structure resembling biological neuronal information processing. The filter is fully configurable, from a 'transparent mode' to a very restrictive mode. Based on an analysis of configuration parameters, three main configurations are given: weak, medium and restrictive. Using data from a DVS event camera, results for a similarity detection algorithm show that event data can be reduced up to 30% while maintaining the same similarity index when compared to unfiltered data. Data reduction can reach 85% with a penalty of 15% in similarity index compared to the original data. An object tracking algorithm was also used to compare results of the proposed filter with other existing filter. The LDSI filter provides less error (4.86 ± 1.87) when compared to the background activity filter (5.01 ± 1.93). The algorithm was tested under a PC using pre-recorded datasets, and its FPGA implementation was also carried out. A Xilinx Virtex6 FPGA received data from a 128 128 DVS camera, applied the LDSI algorithm, created a AER dataflow and sent the data to the PC for data analysis and visualization. The FPGA could run at 177 MHz clock speed with a low resource usage (671 LUT and 40 Block RAM for the whole system), showing real time operation capabilities and very low resource usage. The results show that, using an adequate filter parameter tuning, the relevant information from the scene is kept while fewer events are generated (i.e., fewer generated data).
dc.language.iso	eng
dc.relation.ispartof	Sensors, 2018, vol. 18, num. 12, p. 4122
dc.subject	Enginyeria Disseny
dc.subject	Enginyeria elèctrica
dc.title	LDSI - Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm
dc.type	journal article	es_ES
dc.date.updated	2018-12-14T15:08:27Z
dc.identifier.doi	10.3390/s18124122
dc.identifier.idgrec	128938
dc.rights.accessRights	open access	es_ES