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@article{150057,
        author = {Laxmikant Mukkawar and Akash Deokar and Sarthak Chature and Yashraj Wable and Yashwant Ingle},
        title = {Road Traffic Surveillance System for Helmet Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {2},
        pages = {316-319},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=150057},
        abstract = {Surveillance is very essential for the safety of power substation. The detection of whether wearing safety helmets or not for perambulatory workers is the key component of overall intelligent surveillance system in power substation. In our system, a novel and practical safety helmet detection framework based on computer vision, machine learning and image processing is proposed. In order to ascertain motion objects in power substation, the CNN algorithm is employed. Moreover, based on the result of motion objects segmentation, real-time human classification framework C4 is applied to locate pedestrian in power substation accurately and quickly. Finally, according to the result of pedestrian detection, the safety helmet wearing detection and the send fine notification is implemented using the head location/Vehicle Number plate detection, the color space transformation and the color feature discrimination. Extensive compelling experimental results in power substation illustrate the efficiency and effectiveness of the proposed framework.},
        keywords = {CCTV Surveillance, Traffic congestion, object detection, Classification, CNN algorithm, traffic signal},
        month = {},
        }