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arXiv · 2509.26085

A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing

Yu Chen·Jing Lian·Zhaofei Yu·Jizhao Liu·Jisheng Dang·Gang Wang
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Abstract

Event cameras are bio-inspired vision sensor that encode visual information with high dynamic range, high temporal resolution, and low latency.Current state-of-the-art event stream processing methods rely on end-to-end deep learning techniques. However, these models are heavily dependent on data structures, limiting their stability and generalization capabilities across tasks, thereby hindering their deployment in real-world scenarios. To address this issue, we propose a chaotic dynamics event signal processing framework inspired by the dorsal visual pathway of the brain. Specifically, we utilize Continuous-coupled Neural Network (CCNN) to encode the event stream. CCNN encodes polarity-invariant event sequences as periodic signals and polarity=changing event sequences as chaotic signals. We then use continuous wavelet transforms to analyze the dynamical states of CCNN neurons and establish the high-order mappings of the event stream. The effectiveness of our method is validated through integration with conventional classification networks, achieving state-of-the-art classification accuracy on the N-Caltech101 and N-CARS datasets, with results of 84.3% and 99.9%, respectively. Our method improves the accuracy of event camera-based object classification while significantly enhancing the generalization and stability of event representation. Our code is available in https://github.com/chenyu0193/ACDF.

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Yu Chen, Jing Lian, Zhaofei Yu, Jizhao Liu, Jisheng Dang, Gang Wang. 2025-09-30. A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing. https://arxiv.org/abs/2509.26085

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