WANG Y W, ZHANG J Y, CHEN K Y,et al.Study on the method of converting asynchronous event stream into grid representation[J].Journal of Henan Polytechnic University(Natural Science) ,2025,44(5):17-26.

DOI:10.16186/j.cnki.1673-9787.2024070033

Received: 2024/07/05

Revised: 2024/10/11

Published:2025/07/23

Study on the method of converting asynchronous event stream into grid representation

Wang Yanwei, Zhang Jiayu, Chen Kaiyun, Ren Chunping

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

Abstract: Objectives To address the complexity and sparsity of asynchronous event streams， which complicate data analysis， reduce storage and computational efficiency， a method was proposed to convert asynchronous event stream into grid representation.  Methods Each event was replaced by a Dirac delta function and represented as a set of event fields. Based on tensor characteristics， average measurements were assigned to events missing the same category of information， reducing computation while preserving high dynamic resolution. Usable data were selected， and a multilayer perceptron （MLP） was used to replace manually chosen aggregation kernels to identify optimal measurement functions. In ECTResNet， convolution was performed and dimension was reduced through periodic sampling to retain key information for quantization. The convolved data were discretized in continuous 3D space to generate a fixed-size grid. Finally， the event stream was transformed into a grid representation suitable for deep learning. Results The proposed method was evaluated on the N-Cars and N-Caltech101 datasets. Recognition accuracies reached 97.07% and 87.72%， respectively， improving by 10.09% and 11.44% over the event spike tensor method.  Conclusions Experiments showed that converting asynchronous event stream into grid representation enhanced compatibility with deep learning models， improved accuracy and efficiency of event processing and recognition， and enabled end-to-end representation learning. This approach held broad potential in sensor data processing and event recognition.

Key words:event camera;asynchronous event stream;deep learning;convolutional neural network