[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124133-en":3,"doc-seo-124133-105":30,"detail-sidebar-cat-0-en-105":91},{"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":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":27,"seo_description":14,"update_tm":28,"read_time":29},124133,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Estimating road-user position from a camera - a machine learning approach to enable safety applications","Road user interactions are fundamental to transportation safety, particularly for vulnerable road users who face heightened exposure to accidents and their consequences, including pedestrians, cyclists, and motorcyclists. Naturalistic video data offers a rich basis for analyzing these interactions, but its scale makes manual reduction difficult to sustain. This thesis develops a computer-vision and machine-learning pipeline that automates video data reduction and improves analysis by extracting kinematics. Using lidar as ground truth, the model estimates distance and angle for detected pedestrians from camera footage, reducing manual effort while supporting active safety research.","Estimating road-user position from a camera: a machine learning approach to enable safety applications  \nMaster’s Thesis in Mobility Engineering  \nKaran Bharti  \nDEPARTMENT OF MECHANICS AND MARITIME SCIENCES (M2) DIVISION OF VEHICLE SAFETY  \nChalmers University of Technology Gothenburg, Sweden 2023  \n[www.chalmers.se](www.chalmers.se)  \n.  \nMaster’s thesis in Mobility Engineering  \nEstimating road-user position from a camera: a machine learning approach to enable safety applications  \nkaran bharti  \nDepartment of Mechanics and Maritime Sciences (M2) Division of Vehicle Safety Chalmers University of Technology Gothenburg, Sweden 2023  \nEstimating road-user position from a camera: a machine learning approach to enable safety applications  \nMMSX30-Master’s Thesis  \n© KARAN BHARTI, 2023 .  \nDepartment of Mechanics and Maritime Sciences (M2) Division of Vehicle Safety  \nChalmers University of Technology SE-412 96 Gothenburg Telephone +46 31 772 1000  \nCover: AI algorithm detecting objects with position estimation for pedestrians[9] .  \nTypeset in LATEX Published on [odr.chalmers.se](odr.chalmers.se)[ ](odr.chalmers.se)Gothenburg, Sweden 2023  \nEstimating road-user position from a camera: a machine learning approach to enable safety applications  \nMaster’s thesis in Mobility Engineering Karan Bharti  \nDepartment of Mechanics and Maritime Sciences (M2) Division of Vehicle Safety  \nChalmers University of Technology  \nAbstract  \nRoad user interactions are a crucial aspect of transportation safety, especially for vulnerable road users (VRU) . These individuals are more susceptible to accidentsand their associated consequences. It includes pedestrians, cyclists, and motorcyclists among other road users. It is of paramount importance to analyze road traffic interactions with a special focus on VRU for developing active safety algorithms, effective transportation policies, and safety measures. To this end, researchers have turned to naturalistic video data as a source of information for analyzing road user interactions. Considering the data volume, manual data reduction would be a challenge in scaling the data analysis. This underscores the importance of robust and efficient pipelines for the analysis of huge amounts of naturalistic video data, using computer vision algorithms, to help in understanding traffic interaction. In response, this thesis delves into the realm of computer vision involving machine learning to automate video data reduction and improve the analysis of road user interactions. Leveraging lidar’s accurate 3D spatial information and cameras’ detailed visual data, this thesis aims to develop a machine learning model for extraction of kinematics such as distance and angle of detected VRU with a focus on pedestrians, from video files. The model was trained on lidar output as ground truth for distance and angle estimation.  \nBy developing algorithms capable of extracting position from video data, this thesis aims to streamline the analysis process, reducing manual effort and error-prone subjectivity. This work can help in active safety research to understand road-user interactions and improve traffic safety.  \nKeywords: Active Safety, Video Data Reduction, Machine Learning, Kinematics Extraction, Distance Estimation, Vulnerable Road Users, Camera Calibration.  \nContents  \nAbstract iv  \nPreface vii  \nList of Figures viii  \nList of Tables viii  \n1 Introduction 1  \n1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1  \n1.2 Aims and objective ............................ 2  \n2 Theory 4  \n2.1 Introduction to active safety ....................... 4  \n2.2 Computer vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4  \n2.2. 1 Monocular camera . . . . . . . . . . . . . . . . . . . . . . . . 4  \n2.2.2 Fisheye lens . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5  \n2.2.3 Computer vision algorithms . . . . . . . . . . . . . . . . . . . 5  \n2.2.3.1 You only look once . . . . . . . . . . ","cbCaijR8EDQmTiSr","https://ap.wps.com/l/cbCaijR8EDQmTiSr","pdf",10148045,1,45,"English","en",105,"# Abstract\n# Introduction\n## Background\n## Aims and objective\n# Theory\n## Introduction to active safety\n## Computer vision\n## Lidar\n## Machine learning regression models\n## Model training and evaluation\n# Methods\n## Test vehicle\n## Data collection\n## Data analysis\n## Machine learning models\n# Results\n## Dataset","[{\"question\":\"What problem does the thesis address in transportation safety?\",\"answer\":\"It targets the challenge of analyzing road-user interactions with emphasis on vulnerable road users, for which accidents often have severe consequences.\"},{\"question\":\"How does the proposed approach estimate vulnerable road user position from video?\",\"answer\":\"It uses a machine-learning model that extracts kinematics such as distance and angle from camera video, trained using lidar-derived outputs as ground truth.\"},{\"question\":\"Why is automating video data reduction important here?\",\"answer\":\"Because naturalistic video datasets are large, manual reduction does not scale; robust automated pipelines reduce effort and limit error-prone subjective work.\"}]","Estimating road-user position from a camera - a machine learning approach to enable safety applications | PDF",1785820630,113,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"estimating-road-user-position-from-a-camera-a-machine-learning-approach-to-enable-safety-applications","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/estimating-road-user-position-from-a-camera-a-machine-learning-approach-to-enable-safety-applications/124133/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the thesis address in transportation safety?","Question",{"text":75,"@type":76},"It targets the challenge of analyzing road-user interactions with emphasis on vulnerable road users, for which accidents often have severe consequences.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed approach estimate vulnerable road user position from video?",{"text":80,"@type":76},"It uses a machine-learning model that extracts kinematics such as distance and angle from camera video, trained using lidar-derived outputs as ground truth.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is automating video data reduction important here?",{"text":84,"@type":76},"Because naturalistic video datasets are large, manual reduction does not scale; 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