Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm
| Autor: | Leandro D. Medus, Alfredo Rosado-Muñoz, Juan Barrios-Aviles, Manuel Bataller-Mompeán, Juan F. Guerrero-Martinez |
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| Rok vydání: | 2018 |
| Předmět: |
bioinspired event filtering
Computer science dynamic vision sensor 02 engineering and technology lcsh:Chemical technology 01 natural sciences Biochemistry Article Analytical Chemistry Reduction (complexity) 0202 electrical engineering electronic engineering information engineering neuromorphic systems lcsh:TP1-1185 Electrical and Electronic Engineering Enginyeria Disseny Instrumentation Enginyeria elèctrica Event (computing) Noise (signal processing) 010401 analytical chemistry Filter (signal processing) Atomic and Molecular Physics and Optics 0104 chemical sciences event data reduction FPGA implementation spike-based Lookup table 020201 artificial intelligence & image processing event-based sensors Algorithm Data reduction |
| Zdroj: | 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 Sensors, Vol 18, Iss 12, p 4122 (2018) Sensors (Basel, Switzerland) Sensors Volume 18 Issue 12 RODERIC. Repositorio Institucional de la Universitat de Valéncia instname |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s18124122 |
| Popis: | 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&mdash 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 &ldquo transparent mode&rdquo 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). |
| Databáze: | OpenAIRE |
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