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@article{151342,
        author = {Sethuselvi M and Dr.S.Kayalvizhi},
        title = {Pedestrain Motion Detection using YOLO v3 Algorithm},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {12},
        pages = {441-445},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=151342},
        abstract = {Vehicular accidents claim millions around the world every year most of which that are caused due to human-error. The Autonomous Vehicles are believed to bring solution to the problem by automating the use of vehicles. The autonomous vehicles have a very important feature called pedestrian detection to detect pedestrians on the road and stop in the appropriate places. Many multi-person trackers have been proposed for real-time detection of pedestrians but due to inaccurate predictions and long training time they have not been efficient.In this paper pedestrians are detected using yolo v3 algorithm on the COCO dataset which consists of sequence of images.The images are trained and the output is identified with the help of convolutional neural networks in yolo architecture.},
        keywords = {PMD, YOLO, RCNN, CNN, IOU, NMS.},
        month = {},
        }