[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83922-en":3,"doc-seo-83922-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83922,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","An Event-Driven Framework for Fly-Inspired Visual Motion Detection","Fast, reliable motion detection under dynamic conditions is addressed by integrating event-based sensing with biologically structured neural computation. The framework builds on a fly-inspired neural network that emulates optic-lobe motion circuits, using a feed-forward, training-free design with a small set of interpretable parameters for real-time embedded deployment. Event cameras deliver low-latency, low-power, and high-dynamic-range sensing, but suffer noise such as temporal distortions and junction-leakage activity, especially in low light. A time-surface front end and a fly optic-lobe inspired network estimate foreground motion direction, while bottom-up attention suppresses background motion. Evaluation on real-world ground-vehicle datasets shows effective combination of event-driven temporal advantages with efficient, interpretable bio-inspired processing.","An event-driven framework for fly-inspired visual  \nmotion detection  \nQinbing Fu∗ ,†, 1 , Jingyu Huang†, 1 , Yan Xie 1 , Jigen Peng 1 , Yuchao Tang∗ , 1  \n1 School of Mathematics and Information Science, Guangzhou University, China  \n∗ Corresponding authors: {qifu,[yctang](yctang}@gzhu.edu.cn)[}](yctang}@gzhu.edu.cn)[@gzhu.edu.cn](yctang}@gzhu.edu.cn)[ ](yctang}@gzhu.edu.cn)† The authors contributed equally.  \narXiv :2607 .05205v 1 [ cs .CV] 6 Jul 2026  \nAbstract—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 flyinspired neural network that emulates motion-processing circuitsin 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, lowpower, 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 lowlight conditions. Moreover, effective integration between eventbased 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. These findings support a new computational paradigm for integrating event cameras with structured neural models in real-time motion perception.  \nIndex Terms—Bio-inspired motion detection, Event camera, Time surface, Fly-inspired neural model, Structured intelligence  \nI. INTRODUCTION  \nVisual motion detection is a fundamental capability of both biological and artificial vision systems. It provides essential information for autonomous navigation, collision avoidance, target tracking, and swarm coordination in dynamic environments. Consequently, developing efficient and reliable motiondetection methods remains an important research challenge.  \nRecently, event cameras have emerged as a promising bioinspired sensing paradigm for dynamic vision tasks. Owing to their asynchronous operation, event cameras offer microsecond-level temporal resolution, low latency, and reduced motion blur, making them particularly well suited to  \nThis research was supported by the National Natural Science Foundation of China under grant nos. 62376063, 12571558, 12571491 .  \nmotion analysis. Accordingly, event-based vision has been widely explored for applications such as optical flow (OF) estimation, motion segmentation, and visual navigation, and so forth as surveyed in [13] . For example, [1] proposed an event-wise motion-compensation method for motion segmentation, jointly estimating event-cluster assignments and motion parameters to separate independently moving objects from the background. For OF estimation, [2] incorporated edge information into a contrast-maximization framework, resulting in an edge-informed approach for event-based flow estimation. In addition, [3] addressed real-time object tracking using an eve","cbCaihKYVts4k7as","https://ap.wps.com/l/cbCaihKYVts4k7as","pdf",8259400,1,6,"English","en",105,"# Introduction\n## Background and motivation\n## Related work\n## Challenges\n## Proposed approach","[{\"question\":\"What is the main goal of the proposed motion detection framework?\",\"answer\":\"To achieve fast and reliable visual motion detection by integrating event-based sensing with a fly-inspired, biologically structured neural computation approach for real-time foreground motion-direction estimation.\"},{\"question\":\"How does the framework represent events before neural processing?\",\"answer\":\"It uses a time-surface encoding to form the front-end event representation, which feeds into a fly optic-lobe-inspired neural network.\"},{\"question\":\"What mechanisms are used to improve robustness and suppress irrelevant motion?\",\"answer\":\"Bottom-up attention is incorporated to suppress background motion and enhance the saliency of foreground targets, helping address issues caused by event noise and low-light 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is the main goal of the proposed motion detection framework?","Question",{"text":74,"@type":75},"To achieve fast and reliable visual motion detection by integrating event-based sensing with a fly-inspired, biologically structured neural computation approach for real-time foreground motion-direction estimation.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the framework represent events before neural processing?",{"text":79,"@type":75},"It uses a time-surface encoding to form the front-end event representation, which feeds into a fly optic-lobe-inspired neural network.",{"name":81,"@type":72,"acceptedAnswer":82},"What mechanisms are used to improve robustness and suppress irrelevant motion?",{"text":83,"@type":75},"Bottom-up attention is incorporated to suppress background motion and enhance the saliency of foreground targets, helping address issues caused by event noise and low-light 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