An event-driven framework for fly-inspired visual motion detection

• URL: http://arxiv.org/abs/2607.05205v1
• Date: Mon, 06 Jul 2026 15:22:44 GMT
• Title: An event-driven framework for fly-inspired visual motion detection
• Abstract summary: This work integrates emerging event-based sensing with biologically structured neural computation to establish an efficient computational paradigm for visual motion detection.<n>The proposed framework is built upon a recently developed fly-inspired neural network that emulates motion-processing circuits in the optic lobe.<n> Experimental results demonstrate that the framework effectively combines the temporal advantages of event-driven vision with the efficiency and interpretability of bio-inspired neural processing.
• Score: 9.756242900662377
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Fast and reliable motion detection is essential for machine vision and autonomous systems operating in dynamic environments. This work integrates emerging event-based sensing with biologically structured neural computation to establish an efficient computational paradigm for visual motion detection. The proposed framework is built upon a recently developed fly-inspired neural network that emulates motion-processing circuits in the optic lobe. Owing to its feed-forward and training-free architecture, the neural model requires only a small number of interpretable parameters and is well suited for real-time embedded implementation. Event cameras provide low-latency, low-power, and high-dynamic-range visual sensing by asynchronously transmitting brightness-change events. However, their performance can be degraded by event noise, including temporal noise and junction-leakage-induced activity, particularly under low-light conditions. Moreover, effective integration between event-based visual representations and biologically inspired neural processing remains under-explored. To address these challenges, we propose an event-driven computational framework that combines time-surface encoding for front-end event representation with a fly optic-lobe-inspired neural network for foreground motion-direction estimation. A bottom-up attention mechanism is further incorporated to suppress background motion and enhance the saliency of foreground targets. The proposed method is evaluated on real-world ground-vehicle datasets and compared with a baseline frame-based model and an optimization-based approach. Experimental results demonstrate that the framework effectively combines the temporal advantages of event-driven vision with the efficiency and interpretability of bio-inspired neural processing.

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