[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86554-en":3,"doc-seo-86554-105":30,"detail-sidebar-cat-0-en-105":95},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},86554,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","ASUMOT Motion-Consistency-Based Asynchronous UAV Detection and Tracking with Event Cameras","Event cameras provide microsecond temporal resolution and high dynamic range for low-altitude UAV perception, but long-range UAVs often yield sparse, fragmented, and noise-contaminated event responses that split one semantic target into multiple spatial blobs. Direct blob-level asynchronous tracking then creates duplicate trajectories and unstable identities. ASUMOT proposes a motion-consistency-based asynchronous framework operating on raw events, representing each UAV as a time-varying set of motion-consistent event blobs. It includes a motion-consistency candidate trigger, a lightweight multi-task verifier, and clustering to aggregate fragmented blobs into stable UAV-level tracks. The ES-UAV benchmark offers dense event-level annotations, and experiments demonstrate improved accuracy–efficiency while preserving asynchronous processing.","ASUMOT: Motion-Consistency-Based Asynchronous UAV Detection and Tracking  \nwith Event Cameras  \nBaofeng Jia 12 , Xiaoyu Chen 12∗, Jingyuan Zhang 12 , Zongze Wu 12 , Haochen li 12 , Jing Han 12 , Lianfa  \nBai 12  \n1 State key Laboratory of Extreme Environment Optoelectronic Dynamic Testing Technology and Instrument, Nanjing  \nUniversity of Science and Technology, Nanjing 210094, China  \n2Jiangsu Key Laboratory of Visual Sensing and Intelligent Perception, Nanjing University of Science and Technology, Nanjing  \n210094, China  \n[baofengjia@njust.edu.cn](baofengjia@njust.edu.cn)  \narXiv :2607 . 11303v1 [ cs .CV] 13 Jul 2026  \nAbstract  \nEvent cameras offer microsecond-level temporal resolution and high dynamic range for low-altitude UAV perception. However, long-range UAVs often produce sparse, fragmented, and noise-contaminated event responses, where one semantic target may appear as multiple spatially separated blobs. Direct blob-level asynchronous tracking therefore suffers from duplicate trajectories and unstable identities. We propose ASUMOT, a motion-consistency-based asynchronous UAV detection and tracking framework operating directly on raw events. ASUMOT models each UAV as a set of motionconsistent event blobs. A local motion-consistency estimator triggers reliable candidates, a lightweight multi-task verifier provides UAV confidence and motion-direction cues, and motion-consistency clustering aggregates fragmented blobs into identity-consistent UAV tracks. We also introduce ESUAV, a high-definition event-level UAV benchmark with dense semantic annotations. Experiments on public UAV tracking data and ES-UAV show that ASUMOT improves the accuracy–efficiency trade-off while preserving asynchronous event processing. Code and Dataset will be released.  \nIntroduction  \nThe rapid proliferation of unmanned aerial vehicles (UAVs) has intensified the demand for reliable low-altitude surveillance. In anti-UAV scenarios, long-range targets are often small, fast, and visually ambiguous, making real-time detection–tracking under cluttered backgrounds challenging. Frame-based methods (Li et al. 2021; Gong et al. 2021; Huang et al. 2023; Sun et al. 2024) suffer from motion blur, illumination changes, and large inter-frame displacement in dynamic environments.  \nEvent cameras provide microsecond temporal resolution and high dynamic range (Gallego et al. 2020) . Synchronous event methods convert events into frames, voxel grids, or dense representations (Peng et al. 2024b; Chen et al. 2025; Gehrig and Scaramuzza 2023; Wang et al. 2025), but this introduces latency–accuracy trade-offs and discards finegrained timing. Fully asynchronous trackers (Apps et al. 2025) preserve event-level timing, yet typically promote each salient blob as an independent object. This blob-level assumption is restrictive for UAVs: one semantic UAV may  \n∗Corresponding author.  \nFigure 1: Long-range UAVs produce fragmented blobs, causing duplicate/unstable blob-level tracks. ASUMOT aggregates motion-consistent blobs for stable identity tracking.  \ngenerate multiple fragmented, intermittent, and spatially separated event blobs, causing duplicate trajectories, missed targets, and unstable identities.  \nwe propose ASUMOT, an asynchronous UAV detection and tracking framework operating directly on raw events. The central idea is to separate low-level event-blob hypotheses from high-level UAV identities. ASUMOT does not assume that one event blob corresponds to one physical target. Instead, each UAV is represented as a time-varying set of motion-consistent event blobs. Low-level blob hypotheses are generated and updated asynchronously, while high-level UAV identities are maintained by aggregating fragmented but motion-consistent blob observations.  \nWe also construct ES-UAV, a high-definition event-level UAV benchmark. Its blob-to-event annotation strategy re-  \nduces background mislabeling while preserving target contours and motion trajectories, enabling fine-grained e","cbCaiutnGwHTwkDN","https://ap.wps.com/l/cbCaiutnGwHTwkDN","pdf",10162325,2,1,9,"English","en",105,"# Abstract\n# Introduction\n# Related Work\n## Representation-based event detection and tracking","[{\"question\":\"What problem does ASUMOT target in long-range UAV tracking with event cameras?\",\"answer\":\"Long-range UAVs often generate sparse, fragmented, and noise-contaminated events that produce multiple spatial blobs for one UAV. Blob-level asynchronous tracking then yields duplicate trajectories and unstable identities.\"},{\"question\":\"How does ASUMOT represent a UAV instead of using a single blob track?\",\"answer\":\"ASUMOT models each UAV as a time-varying set of motion-consistent event blobs. It separates low-level blob hypotheses from high-level UAV identity maintenance by aggregating fragmented but motion-consistent observations.\"},{\"question\":\"What modules in ASUMOT help convert fragmented events into stable UAV tracks?\",\"answer\":\"ASUMOT uses a local motion-consistency estimator to trigger reliable candidates, a lightweight multi-task verifier to provide UAV confidence and motion-direction cues, and motion-consistency clustering to aggregate fragmented blobs into identity-consistent tracks.\"},{\"question\":\"What is ES-UAV and how does it support evaluation of the proposed approach?\",\"answer\":\"ES-UAV is a high-definition, event-level UAV benchmark with dense semantic annotations using a blob-to-event annotation strategy. It reduces background mislabeling while preserving target contours and motion trajectories for fine-grained event-domain evaluation.\"}]",1784212599,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"asumot-motion-consistency-based-asynchronous-uav-detection-and-tracking-with-event-cameras","",{"@graph":36,"@context":89},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/asumot-motion-consistency-based-asynchronous-uav-detection-and-tracking-with-event-cameras/86554/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does ASUMOT target in long-range UAV tracking with event cameras?","Question",{"text":75,"@type":76},"Long-range UAVs often generate sparse, fragmented, and noise-contaminated events that produce multiple spatial blobs for one UAV. Blob-level asynchronous tracking then yields duplicate trajectories and unstable identities.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does ASUMOT represent a UAV instead of using a single blob track?",{"text":80,"@type":76},"ASUMOT models each UAV as a time-varying set of motion-consistent event blobs. It separates low-level blob hypotheses from high-level UAV identity maintenance by aggregating fragmented but motion-consistent observations.",{"name":82,"@type":73,"acceptedAnswer":83},"What modules in ASUMOT help convert fragmented events into stable UAV tracks?",{"text":84,"@type":76},"ASUMOT uses a local motion-consistency estimator to trigger reliable candidates, a lightweight multi-task verifier to provide UAV confidence and motion-direction cues, and motion-consistency clustering to aggregate fragmented blobs into identity-consistent tracks.",{"name":86,"@type":73,"acceptedAnswer":87},"What is ES-UAV and how does it support evaluation of the proposed approach?",{"text":88,"@type":76},"ES-UAV is a high-definition, event-level UAV benchmark with dense semantic annotations using a blob-to-event annotation strategy. 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