[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86577-en":3,"doc-seo-86577-105":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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"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},86577,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Event-RGB Adaptive Tracking for Nighttime Highway Perception","Highway Intelligent Transportation Systems commonly rely on conventional RGB cameras for traffic perception and vehicle tracking, but nighttime operation suffers from motion blur, insufficient exposure, and low signal-to-noise ratios, causing severe reliability degradation. A Joint Event-RGB Adaptive Tracking (JEAT) framework is introduced to jointly associate asynchronous event streams and RGB frames through unified data association optimization. An Adaptive Extended Kalman Filter estimates measurement noise via NIS statistics to dynamically weight and fuse modalities. A large synthetic dataset, SEHN, is built in CARLA with synchronized RGB and event data under varied day/night and traffic-density conditions.","Event-RGB Adaptive Tracking for Nighttime Highway Perception  \nHaidong Wang 1 , Hengxing Cai 1 , Wanlei Li2 , Xiaogang Xiong2 , and Renxin Zhong 1,∗  \narXiv :2607 . 11646v1 [ cs .CV] 13 Jul 2026  \nAbstract—Intelligent Transportation Systems deployed on highways predominantly rely on conventional RGB cameras for traffic perception and vehicle tracking. However, highway environments present unique challenges: the absence of artificial lighting infrastructure, combined with high vehicle velocities, results in severely degraded perception performance under low-light conditions. Specifically, nighttime scenarios suffer from motion blur, insufficient exposure, and poor signalto-noise ratios, which catastrophically impair the reliability of RGB-based sensing systems. To address these limitations, we propose a novel Joint Event-RGB Adaptive Tracking (JEAT) framework. Unlike existing multi-sensor trackers constrained by rigid, hard-coded prioritization, JEAT merges asynchronous event streams and RGB frames into a unified joint data association optimization. By employing an Adaptive Extended Kalman Filter to continuously estimate measurement noise via NIS statistics, the framework dynamically weights and fuses both modalities, optimally harnessing event streams during dark or high-speed motion while leveraging RGB frames under bright or static conditions. Furthermore, given the absence of publicly available datasets tailored for event-based highway perception with diverse environmental conditions, we present SEHN, a large-scale synthetic dataset generated using the CARLA simulator. Our dataset encompasses diverse environmental conditions (daytime, nighttime, nighttime with out artificial lighting) and varying traffic densities, providing synchronized RGB imagery and event streams to facilitate multimodal fusion research. Our code and datasets will be available at [https://github.com/haidongwang96/SEHN](https://github.com/haidongwang96/SEHN).  \nI. INTRODUCTION  \nThe rapid expansion of highway networks worldwide has driven an increasing demand for robust and reliable Intelligent Transportation Systems (ITS) capable of continuous traffic monitoring, incident detection, and vehicle tracking. Contemporary ITS infrastructures predominantly employ RGB camera-based perception systems, leveraging advancesin deep learning for object detection and tracking. By capturing high-resolution visual streams, RGB surveillance cameras enable the precise extraction of multi-dimensional static attributes, such as vehicle models, license plates, and colors, as well as dynamic motion states including instantaneous velocity, traffic volume, and queue length. Through semantic analysis of spatiotemporal sequences, the system further facilitates the automated identification of complex traffic events, such as violations or accidents, and provides quantitative evaluations of the Level of Service for road  \n1Haidong Wang, Hengxing Cai, and Renxin Zhong are with the School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China. {wanghd26,[caihx3](caihx3}@mail2.sysu.edu.cn)[}](caihx3}@mail2.sysu.edu.cn)[@mail2.sysu.edu.cn](caihx3}@mail2.sysu.edu.cn) , [zhrenxin@mail.sysu.edu.cn](zhrenxin@mail.sysu.edu.cn)  \n2Wanlei Li and Xiaogang Xiong are with the School of Intelligence Science and Engineering, Harbin Institute of Technology, Shenzhen, China.  \n[19b953034@stu.hit.edu.cn](19b953034@stu.hit.edu.cn) , [xiongxg@hit.edu.cn](xiongxg@hit.edu.cn)  \n∗ Corresponding author: Renxin Zhong.  \n(a) Ego motion nighttime highway driving scenario  \n(b) Static nighttime highway traffic surveillance scenario  \nFig. 1: Nighttime highway perception scenarios from (a) egomotion and (b) static surveillance perspectives. Under lowlight conditions with high-speed traffic, conventional RGB cameras suffer from severe motion blur and underexposure, rendering them nearly ineffective for reliable perception  \nnetworks. These high-level data provide essential support for traffi","cbCaivF7rXr1wrIp","https://ap.wps.com/l/cbCaivF7rXr1wrIp","pdf",7422615,4,1,"English","en",105,"# Introduction\n## Challenges of RGB tracking at night\n## Rationale for event cameras and multimodal fusion\n## Proposed JEAT framework (overview)","[{\"question\":\"Why do conventional RGB cameras struggle with nighttime highway perception and tracking?\",\"answer\":\"Nighttime environments combine extremely low ambient illumination with high vehicle speeds, leading to motion blur, underexposure, and poor contrast, which undermine feature extraction and temporal association and cause missed detections, identity switches, and fragmented trajectories.\"},{\"question\":\"What is the core idea of the JEAT framework?\",\"answer\":\"JEAT performs unified joint data association optimization by combining asynchronous event streams with RGB frames, avoiding rigid hard-coded sensor prioritization.\"},{\"question\":\"How does JEAT adaptively fuse event and RGB measurements over time?\",\"answer\":\"JEAT uses an Adaptive Extended Kalman Filter to continuously estimate measurement noise using NIS statistics, which dynamically updates the weighting between modalities under dark/high-speed versus bright/static 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do conventional RGB cameras struggle with nighttime highway perception and tracking?","Question",{"text":74,"@type":75},"Nighttime environments combine extremely low ambient illumination with high vehicle speeds, leading to motion blur, underexposure, and poor contrast, which undermine feature extraction and temporal association and cause missed detections, identity switches, and fragmented trajectories.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What is the core idea of the JEAT framework?",{"text":79,"@type":75},"JEAT performs unified joint data association optimization by combining asynchronous event streams with RGB frames, avoiding rigid hard-coded sensor prioritization.",{"name":81,"@type":72,"acceptedAnswer":82},"How does JEAT adaptively fuse event and RGB measurements over time?",{"text":83,"@type":75},"JEAT uses an Adaptive Extended Kalman Filter to continuously estimate measurement noise using NIS statistics, which dynamically updates the weighting between 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