{"ID":5937006,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-09T15:22:02.48613467Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05205","arxiv_id":"2607.05205","title":"An event-driven framework for fly-inspired visual motion detection","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.","short_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 framew...","url_abs":"https://arxiv.org/abs/2607.05205","url_pdf":"https://arxiv.org/pdf/2607.05205v1","authors":"[\"Qinbing Fu\",\"Jingyu Huang\",\"Yan Xie\",\"Jigen Peng\",\"Yuchao Tang\"]","published":"2026-07-06T15:22:44Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.NE\"]","methods":"[]","has_code":false}