WANG Y W，ZHANG J Y，CHENG K Y,REN C P. Methods Research for Converting Asynchronous Event Streams into Grid Representations [J]. Journal of Henan Polytechnic University( Natural Science), doi:10.16186/j.cnki.1673- 9787. 2024070033. 　

doi: 10.16186/j.cnki.1673-9787. 2024070033

Received：2024-07-05

Revised：2024-07-25

Online：2025-05-08

Methods Research for Converting Asynchronous Event Streams into Grid Representations (Online)

WANG Yanwei, ZHANG Jiayu，CHEN Kaiyun，REN Chunping

(College of Mechanical Engineering, Heilongjiang University of Science &Technology,Harbin 150022,Heilongjiang, China)

Abstract: Objectives To addressing the complexity and sparsity of data analysis, as well as the low storage and computational efficiency，which caused by asynchronous event streams. Methods A method is proposed to convert asynchronous event streams into grid representations.Firstly, Dirac pulse is used to replace each event by a function form, which is represented as an event field set. In order to decreasing the computational cost and holding the high dynamic event resolution, Assigning to each event that loses the most information of a category an average measurement  that category based on the tensor properties of the event. Secordly, the multi-layer perceptron MLP, which can directly use the data to find the best candidate value, is selected by measurement function instead of manually selected aggregate kernel function. It convoluted in ECTResNet to reducing dimensionality and keep key information for quantization through regular sampling. Finally, according to the reconvolution , events are gridded, and a fixed-size grid is generated through continuous three-dimensional space discretization, the final gridded event stream is converted into a grid representation ,which can be used for deep learning as the network output. Results The results are acquired by two public datasets N-Cars and N-Caltech101, and the output representation recognition accuracy of the transformed datasets reach 97.07% and 87.72%, which are higher than the event spike tensor method, which are 10.09% and 11.44% . Conclusions Experiments show that converting asynchronous event streams into grid representations can better adapt to deep learning models, it improve the accuracy and efficiency, which events are processed and recognized. And it support end-to-end representation learning, which can be widely used in the field of sensor data processing and event recognition.

Key words：event camera; asynchronous event streams; deep learning; convolutional neural networks

CLC: TP242