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

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).

Sensor Signal and Information Processing II

“…The reduction in generated data implies the possibility of lower hardware requirements and less power consumption for hardware devices. In this context, Barrios-Aviles et al [ 7 ] have proposed a filtering algorithm which reduces the generated data from event-based sensors without the loss of relevant information. It is a bioinspired filter and event data are processed using a structure resembling biological neuronal information processing.…”

Event Density Based Denoising Method for Dynamic Vision Sensor

“…The output of events is asynchronous, rather than the synchronous output of traditional sensors in frame. Such characteristics make DVS more advantageous in areas such as moving target detection, simultaneous localization and mapping (SLAM), and drones [6][7][8][9][10][11][12][13]. At present, commercial companies have used them for automotive and other fields [14,15].…”

An 8.8 ps RMS Resolution Time-To-Digital Converter Implemented in a 60 nm FPGA with Real-Time Temperature Correction

“…When propagation delay changes exceeded a certain threshold, the bin-by-bin correction was launched, and LUT was updated on the threshold. Thus, the temperature correction scheme could be considered as an event-based control approach [22][23][24][25][26][27]. The proposed scheme was validated in a two-channel non-uniform multiphase (NUMP) TDC [28] using an Altera 60 nm Cyclone 10 LP FPGA and achieved a resolution of 8.8 ps root mean square (RMS) over a wide temperature range from 5 • C to 80 • C.…”