[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119648-en":3,"doc-seo-119648-105":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":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":27,"seo_description":14,"update_tm":28,"read_time":29},119648,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Human Silhouette Detection in Images using Machine Learning - Master of Engineering Thesis","Human silhouette detection has become a key machine learning task with direct value for surveillance, human–computer interaction, healthcare monitoring, and autonomous navigation. Reliable silhouette extraction improves safety while enhancing accessibility and usability in systems operating in real-world environments. This thesis presents a practical machine learning framework that learns visual patterns from data rather than relying on hand-crafted rules. It targets natural settings with clutter and occlusions and builds a custom annotated dataset to reflect diverse backgrounds, postures, and varying visibility levels.","Human silhouette detection in images using  \nmachine learning  \nThesis submitted in partial fulfillment ofthe requirements for the award of degree  \nof  \nMaster of Engineering  \nin  \nComputer Science and Engineering  \nSubmitted By  \nAkbar Ali Ahamed  \n(Roll No. 802332007)  \nUnder the supervision of:  \nDr. H.S Pannu  \n(Assistant Professor)  \nDr. Sanjeev Rao  \n(Assistant Professor)  \nCOMPUTER SCIENCE AND ENGINEERING DEPARTMENT THAPAR INSTITUTE OF ENGINEERING AND TECHNOLOGY  \nPATIALA – 147004  \nCERTIFICATE  \nI hereby certify that the work which is being presented in the thesis entitled,“Human Silhouette detection in images using Machine Learning”, in partial fulfillment of the requirements for the award of degree of Master of Engineering in Software Engineering/ Computer Science and Engineering submitted in Computer Science and Engineering Department of Thapar Institute of Engineering and Technology, Patiala, is an authentic record of my own work carried out under the supervision of Dr. H.S Pannu and Dr. Sanjeev Rao and refers other researcher ’s work which are duly listed in the reference section. The matter presented in the thesis has not been submitted for the award of any other degree of this or any other University.  \n(Akbar Ali Ahamed)  \nThis is to certify that the above statement made by the candidate is correct and true to the best of my knowledge.  \nDr. H. S Pannu Assistant Professor  \nDr. Sanjeev Rao Assistant Professor  \nAcknowledgement  \nI express my sincere gratitude to all those who have supported and guided me throughout my thesis work. This research would not have been possible without the encouragement, insights, and assistance I received from several individuals and institutions. First and foremost, I am very grateful to my respected supervisors, Dr. H.S. Pannu and Dr. Sanjeev Rao, for their invaluable guidance, encouragement, and constant support. Their expertise, constructive feedback, and timely suggestions were instrumental in shaping the direction of this research and bringing it to its present form. I am truly fortunate to have had the opportunity to work under their mentorship. I also extend my heartfelt appreciation to the faculty members and staff of the Department of CSE, Thapar Institute of Engineering and Technology (TIET), for providing a supportive academic environment and access to essential resources and facilities. My sincere thanks go tomy fellow researchers, colleagues, and friends who have contributed in various ways by offering assistance, motivation, and sharing their knowledge and experiences. I am especially grateful tomy family for their unwavering encouragement, patience, and belief in my capabilities throughout this academic journey. Their support has been a constant source of strength. Lastly, I wish to acknowledge all those whose names may not have been mentioned here but who have, directly or indirectly, contributed to the successful completion of this thesis. Each contribution, no matter how small, has been truly appreciated. With deep respect and gratitude, I dedicate this work to all who have made this endeavor a fulfilling experience.  \nAbstract  \nDetecting human outlines, or silhouettes, has emerged as a crucial task in the field of machine learning, with important applications in areas such as surveillance, human–computer interaction, healthcare monitoring, and autonomous navigation. Accurate silhouette detection is essential not only for ensuring safety but also for improving accessibility and user experience in systems designed to assist individuals in real-world environments. Unlike traditional computer vision techniques that rely on hand-crafted rules, modern machine learning models—particularly convolutional neural networks (CNNs)—are capable of learning visual patterns from data, making them more effective in handling complex and cluttered scenes. This research introducesa practical machine learning framework for human silhouette detection, focusing on identifying indivi","cbCaieZfnXSJ90TD","https://ap.wps.com/l/cbCaieZfnXSJ90TD","pdf",10735774,1,41,"English","en",105,"# 1. Introduction\n## 1.1 Existing Challenges\n## 1.2 Objectives\n## 1.3 Roadmap of the Thesis\n# 2. Literature Review\n# 3. Research Gaps\n# 4. Methodology\n## 4.1 Fundamentals of Deep Learning and CNNs\n## 4.2 CNN-Based Object Detection\n## 4.3 YOLOv8n Architecture\n## 4.4 DETR R50 Transformer-Based Mo","[{\"question\":\"What problem does the thesis focus on?\",\"answer\":\"The thesis focuses on detecting human silhouettes (human outlines) in images using machine learning models for real-world, cluttered scenes.\"},{\"question\":\"Why is a custom dataset used in the study?\",\"answer\":\"A custom annotated dataset is created to represent diverse backgrounds, human postures, and different visibility conditions, enabling realistic training and evaluation.\"},{\"question\":\"Which deep learning models are compared, and what is their main distinction?\",\"answer\":\"The study compares YOLOv8n and DETR. YOLOv8n is lightweight and designed for real-time performance, while DETR uses transformer-based attention to capture global context and better handle occlusions and overlapping figures.\"}]","Human Silhouette Detection in Images using Machine Learning - Master of Engineering Thesis | PDF",1785725467,103,{"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},"human-silhouette-detection-in-images-using-machine-learning-master-of-engineering-thesis","",{"@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/human-silhouette-detection-in-images-using-machine-learning-master-of-engineering-thesis/119648/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the thesis focus on?","Question",{"text":75,"@type":76},"The thesis focuses on detecting human silhouettes (human outlines) in images using machine learning models for real-world, cluttered scenes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is a custom dataset used in the study?",{"text":80,"@type":76},"A custom annotated dataset is created to represent diverse backgrounds, human postures, and different visibility conditions, enabling realistic training and evaluation.",{"name":82,"@type":73,"acceptedAnswer":83},"Which deep learning models are compared, and what is their main distinction?",{"text":84,"@type":76},"The study compares YOLOv8n and DETR. 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