• Manyala prathyusha
• G. parameshwari
• Ch.sukumar
• K.murali M.tech(PHD)

In order to ensure the safety measures, the detection of traffic rule violators is a highly desirable but challenging task due to various difficulties such as occlusion, illumination, poor quality of surveillance images, varying whether conditions, etc. In this paper, we present a framework for automatic detection of motorcyclists driving without helmets in surveillance images. In the proposed approach, first we use adaptive background subtraction on images frames to get moving objects. Later convolutional neural network (CNN) is used to select motorcyclists among the moving objects. Again, we apply CNN on upper one fourth part for further recognition of motorcyclists driving without a helmet. The performance of the proposed approach is evaluated on two datasets, IIT H Helmet 1 contains sparse traffic and IIT H Helmet 2 contains dense traffic, respectively.

detection , convolutional neural network ,

"DETECTION OF MOTORCYCLISTS WITHOUT HELMET IN IMAGES USING CONVOLUTIONAL NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.159-163, April-2019, Available :http://www.jetir.org/papers/JETIR1904H32.pdf

"DETECTION OF MOTORCYCLISTS WITHOUT HELMET IN IMAGES USING CONVOLUTIONAL NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp159-163, April-2019, Available at : http://www.jetir.org/papers/JETIR1904H32.pdf