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Synopsis
This project presents an advanced, AI-powered surveillance system specifically engineered to enhance road safety and enforce traffic regulations concerning two wheeler riders. The system autonomously detects helmet violations and illegal triple-riding-two of the most prevalent and hazardous traffic infractions involving motorcycles-using a synergy of real-time video analysis and deep learning techniques. At the core of the detection pipeline is YOLOv8, a state-of-the-art object detection model acclaimed for its high-speed inference and remarkable accuracy, enabling it to identify motorcyclists, count riders, and determine helmet usage with precision in live video feeds. The visual data is processed using OpenCV, which captures and refines each frame for effective object detection. In tandem with this, the system incorporates an Automatic Number Plate Recognition (ANPR) module, powered by Easy OCR, to accurately extract license plate information from detected vehicles once a violation is confirmed.
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Traffic Vehicle Monitoring Using CNN
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LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6209583245
ISBN 13: 9786209583247
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Traffic Vehicle Monitoring Using CNN
Published by
LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6209583245
ISBN 13: 9786209583247
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Traffic Vehicle Monitoring Using CNN
Published by
LAP Lambert Academic Publishing, 2026
ISBN 10: 6209583245
ISBN 13: 9786209583247
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Softcover
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TRAFFIC VEHICLE MONITORING USING CNN
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Omniscriptum, LAP Lambert Academic Publishing, 2026
ISBN 10: 6209583245
ISBN 13: 9786209583247
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This project presents an advanced, AI-powered surveillance system specifically engineered to enhance road safety and enforce traffic regulations concerning two wheeler riders. The system autonomously detects helmet violations and illegal triple-riding-two of the most prevalent and hazardous traffic infractions involving motorcycles-using a synergy of real-time video analysis and deep learning techniques. At the core of the detection pipeline is YOLOv8, a state-of-the-art object detection model acclaimed for its high-speed inference and remarkable accuracy, enabling it to identify motorcyclists, count riders, and determine helmet usage with precision in live video feeds. The visual data is processed using OpenCV, which captures and refines each frame for effective object detection. In tandem with this, the system incorporates an Automatic Number Plate Recognition (ANPR) module, powered by Easy OCR, to accurately extract license plate information from detected vehicles once a violation is confirmed. 76 pp. Englisch. Seller Inventory # 9786209583247
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TRAFFIC VEHICLE MONITORING USING CNN
Published by
LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6209583245
ISBN 13: 9786209583247
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Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This project presents an advanced, AI-powered surveillance system specifically engineered to enhance road safety and enforce traffic regulations concerning two wheeler riders. The system autonomously detects helmet violations and illegal triple-riding-two of the most prevalent and hazardous traffic infractions involving motorcycles-using a synergy of real-time video analysis and deep learning techniques. At the core of the detection pipeline is YOLOv8, a state-of-the-art object detection model acclaimed for its high-speed inference and remarkable accuracy, enabling it to identify motorcyclists, count riders, and determine helmet usage with precision in live video feeds. The visual data is processed using OpenCV, which captures and refines each frame for effective object detection. In tandem with this, the system incorporates an Automatic Number Plate Recognition (ANPR) module, powered by Easy OCR, to accurately extract license plate information from detected vehicles once a violation is confirmed. Seller Inventory # 9786209583247
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Traffic Vehicle Monitoring Using CNN (Paperback)
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LAP Lambert Academic Publishing, 2026
ISBN 10: 6209583245
ISBN 13: 9786209583247
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Seller: CitiRetail, Stevenage, United Kingdom
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Paperback. Condition: new. Paperback. This project presents an advanced, AI-powered surveillance system specifically engineered to enhance road safety and enforce traffic regulations concerning two wheeler riders. The system autonomously detects helmet violations and illegal triple-riding-two of the most prevalent and hazardous traffic infractions involving motorcycles-using a synergy of real-time video analysis and deep learning techniques. At the core of the detection pipeline is YOLOv8, a state-of-the-art object detection model acclaimed for its high-speed inference and remarkable accuracy, enabling it to identify motorcyclists, count riders, and determine helmet usage with precision in live video feeds. The visual data is processed using OpenCV, which captures and refines each frame for effective object detection. In tandem with this, the system incorporates an Automatic Number Plate Recognition (ANPR) module, powered by Easy OCR, to accurately extract license plate information from detected vehicles once a violation is confirmed. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9786209583247
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TRAFFIC VEHICLE MONITORING USING CNN
Published by
LAP LAMBERT Academic Publishing Feb 2026, 2026
ISBN 10: 6209583245
ISBN 13: 9786209583247
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 76 pp. Englisch. Seller Inventory # 9786209583247
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TRAFFIC VEHICLE MONITORING USING CNN
Published by
LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6209583245
ISBN 13: 9786209583247
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Seller: preigu, Osnabrück, Germany
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Taschenbuch. Condition: Neu. TRAFFIC VEHICLE MONITORING USING CNN | Tejas Mali (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786209583247 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Seller Inventory # 134745819
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