• K.V.Lalitha
• N. Ragha Sri
• G. Mohan Krishna
• K. Nithin Aaron
• K. Sumanth Babu

As the ratio of motorcycles to people in India continues to rise. Without a helmet, a motorcyclist's risk of dying is 2.5 times that of someone wearing one. While the provided video observation based system has the potential to be rather effective, it still relies heavily on human assistance, the efficiency of which decreases with time and is further impacted by the presence of bias. The authors of this research want to find a solution to this conundrum by developing an algorithm to identify cyclists who are and are not wearing helmets. Video of traffic in a public area is used as training data, with the system able to identify vehicles and pedestrians. This paper suggests a system that would operate by tracking the whereabouts of various riders who go on a journey without helmets. The suggested technique uses a support vector machine (SVM) model, which is a consistent form of SVM model, the leading methodology for object differentiating, to identify cyclists from the outset, and to then differentiate between those wearing and those not wearing helmets. If there are more than two riders, the binary picture is vertically projected for a count. In many nations, the number of people injured or killed on motorcycles has increased dramatically over the years. More than 37 million people in India ride motorcycles. That's why it's crucial to have a system that can automatically identify whether a helmet is being worn or when a rider is taking three seats.Therefore, a Machine learning based approach is used to develop a unique object identification model that can identify motorcyclists. If the cyclists were found to be without helmets and to be travelling in groups of three or more, then the appropriate action must be taken. This study presents a method in which several riders go on a journey while without wearing protective headgear. The proposed method begins with recognising bikers using a consistent SVM model, the cutting-edge methodology for object distinguishing, which in turn aids in differentiating between helmeted and unhelmeted riders as well as between solo and group rides, before sending an SMS via GSM module to the appropriate authorities

SVM, Human Assistance, RFID, GSM, Arduino

"SURVEILLANCE SYSTEM FOR MONITORING BIKE RIDERS WITHOUT HELMET AND TRIPLE RIDERS USING MATLAB", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 2, page no.d151-d156, February-2023, Available :http://www.jetir.org/papers/JETIR2302317.pdf

"SURVEILLANCE SYSTEM FOR MONITORING BIKE RIDERS WITHOUT HELMET AND TRIPLE RIDERS USING MATLAB", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 2, page no. ppd151-d156, February-2023, Available at : http://www.jetir.org/papers/JETIR2302317.pdf