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@article{143960,
        author = {K.RANI and DR.E.V.KRISHNARAO},
        title = {Automatic Highway Vehicle Counting and Classification  for Surveillance Applications},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {3},
        number = {4},
        pages = {212-218},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=143960},
        abstract = {In this we paper are proposing an algorithm   to count and classify highway vehicles based on moving object detection and cascaded regression analysis.  In this algorithm it requires tracking of individual vehicles. Here we are using Back ground subtraction techniques for Back ground and foreground separation. Then we are extracting a set off low level features for each foreground segment by using GLCM. and we developed  a cascaded regression approach to count and  classification. Here we classify vehicles as large, small, and medium. The final count of the number of vehicles passed through the path of choice will be displayed and classified throughout the day. Experimental results of the proposed algorithm show that better performance than the existing algorithms. The classification accuracy is better while comparing with the other algorithms},
        keywords = {Regression analysis, background subtraction, Blob Detection},
        month = {},
        }