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This paper presents a robust real-time embedded platform that tracks loss of attention during both day and night driving. PERCLOS is used as the alertness indicator, with face detection via Haar-like features and Kalman-filter tracking. Eye detection switches between PCA for daytime and block LBP for nighttime, while SVM classifies open vs. closed eye states. Rotation and illumination variations are compensated, cross-validated with brain signals, and deployed on an Intel Atom single board computer, demonstrating robustness in actual driving.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-vision-based-system-for-monitoring-the-loss-of-attention-in-automotive-drivers-abstract/296811/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-vision-based-system-for-monitoring-the-loss-of-attention-in-automotive-drivers-abstract/296811.png","ImageObject",300,407,{"name":92,"@type":93},"Mali","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-18",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What indicator is used to estimate driver alertness in this system?","Question",{"text":113,"@type":114},"The system uses PERcentage of eye CLOSure (PERCLOS) as the indicator of the driver’s alertness level.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How are eyes detected during day and night driving conditions?",{"text":118,"@type":114},"During day time, eyes are detected using Principal Component Analysis (PCA), while during night time the method uses block Local Binary Pattern (LBP) features.",{"name":120,"@type":111,"acceptedAnswer":121},"What is the role of SVM in the pipeline?",{"text":122,"@type":114},"Support Vector Machines (SVM) classify the eye state as open or closed based on the detected eye features.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},296811,1790304968,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},2336475104362,"https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868","A Vision Based System for Monitoring the Loss of Attention in Automotive Drivers  \nAnirban Dasgupta, Anjith George, S L Happy and Aurobinda Routray, Member, IEEE  \nAbstract— On-board monitoring of the alertness level of an automotive driver has been a challenging research in transportation safety and management. In this paper, we propose a robust real-time embedded platform to monitor the loss of attention of the driver during day as well as night driving conditions. The PERcentage of eye CLOSure (PERCLOS) has been used as the indicator of the alertness level. In this approach, the face is detected using Haar-like features and tracked using a Kalman Filter. The Eyes are detected using Principal Component Analysis (PCA) during day time and the block Local Binary Pattern (LBP) features during night. Finally the eye state is classified as open or closed using Support Vector Machines (SVM). In-plane and off-plane rotations of the driver’s face have been compensated using Affine and Perspective Transformation respectively. Compensation in illumination variation is carried out using Bi-Histogram Equalization (BHE). The algorithm has been cross-validated using brain signals and finally been implemented on a Single Board Computer (SBC) having Intel Atom processor, 1 GB RAM, 1.66 GHz clock, x86 architecture, Windows Embedded XP operating system. The system is found to be robust under actual driving conditions.  \nIndex Terms— Driver’s alertness, Object detection, PERCLOS, Haar classifier, PCA, LBP, SVM, Kalman Filter  \nI. INTRODUCTION  \nLOSS  \nto be  \nof attention in automotive drivers has been reported a major cause of road accidents [1], [2], [3], [4], [5] .  \nHence, to prevent such accidents, on-board monitoring of the alertness level in automotive drivers is necessary. Alertness level can be assessed using different measures [6] such as Electroencephalogram (EEG) signals [7], ocular features [8], blood samples [8], speech [9], [10] and others. The EEG based method has been reported to be highly authentic for estimating the state of drowsiness [7] . However, on-board measurement  \nThis work is supported in part by the Department of Electronics and Information Technology, Ministry of Communications and Information Technology, Government of India.  \nAnirban Dasgupta is an MS student in the Electrical Engineering Department, Indian Institute of Technology, Kharagpur, India (e-mail: [anirban1828@gmail.com](anirban1828@gmail.com)).  \nAnjith George is a Research Scholar in the Electrical Engineering Department, Indian Institute of Technology, Kharagpur, India (e-mail: [anjith2006@gmail.com](anjith2006@gmail.com)).  \nS. L. Happy is an MS student in the Electrical Engineering Department, Indian Institute of Technology, Kharagpur, India (e-mail: [happpyhoppy@gmail.com](happpyhoppy@gmail.com)).  \nAurobinda Routray is working as Professor in the Electrical Engineering Department, Indian Institute of Technology, Kharagpur, India (e-mail: [aroutray@iitkgp.ac.in](aroutray@iitkgp.ac.in)).  \nof EEG signals may cause discomfort to the driver and also cause artefacts. In [11] Qiang Ji reported a review of noninvasive techniques to monitor human fatigue. The report states that the potential non-invasive fatigue monitoring measures are eyelid movements, eye gaze, head movement, and facial expressions. The vision-based approaches, being non-contact in nature, appear to be promising for estimating the aforementioned parameters. There has been some studies [5], [12], [13], [14] with regard to eyelid movements such as blink frequency, Average Eye-Closure Speed (AECS), PERCLOS, [etc. as](etc. as) quantitative measures of the drowsiness level of an individual. Of them, PERCLOS is reported to bethe best and most robust measure for fatigue detection [15],[16] . PERCLOS as a drowsiness metric was established in [12] by Wierwille et al. The authenticity of PERCLOS as an effective indicator of drowsiness level has been validated in [17] [18] . PERCLOS can be estim","cbCaidK7p4p4jaKP","https://ap.wps.com/l/cbCaidK7p4p4jaKP","pdf",1115724,14,"English","# I. Introduction\n## Loss of attention and accident motivation\n## Related measures and PERCLOS background\n# II. Proposed vision-based monitoring approach\n## Face detection and tracking pipeline\n## Day/night eye detection and feature extraction\n## Eye state classification with SVM\n## Rotation and illumination compensation\n## Cross-validation and embedded implementation","[{\"question\":\"What indicator is used to estimate driver alertness in this system?\",\"answer\":\"The system uses PERcentage of eye CLOSure (PERCLOS) as the indicator of the driver’s alertness level.\"},{\"question\":\"How are eyes detected during day and night driving conditions?\",\"answer\":\"During day time, eyes are detected using Principal Component Analysis (PCA), while during night time the method uses block Local Binary Pattern (LBP) features.\"},{\"question\":\"What is the role of SVM in the pipeline?\",\"answer\":\"Support Vector Machines (SVM) classify the eye state as open or closed based on the detected eye features.\"}]","A Vision Based System for Monitoring the Loss of Attention in Automotive Drivers - Abstract | PDF",1789737296,35]