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Counting the number of persons in a public space provides valuable intelligence for live as well as recorded-video based monitoring and surveillance applications. When we take the case of diagonal camera setup, counting is achieved by identifying individuals or by mathematically establishing connections between values of simple image properties to the number of persons. In the current System, a people’s head finder could be utilized for assessing the dimensionally changing head measurement, that is the important element utilized in our people estimation strategy. Here our idea makes use of the best-in-class C-N-N for the sparse head location in thick group of people. After dividing the given image into rectangular patches, we employ S-U-R-F feature-based S-V-M binary classifier to name every single box as having people and not-having people and remove all empty boxes. In the current framework, task generally experiences numerous issues, similar to the absence of ongoing handling of the recorded recordings or the event of mistakes because of unimportant individuals being tallied. Our proposed system overcomes the mentioned problems with a state-of-the-art real-time person counting approach referred as YOLO based People Counting. In our proposed system, after the specific pre-treatment, adaptable segmentation and feature production for the people-counting data, the feature vector is leveraged as the input of the trained YOLO to segregate and provide the statistics of the overall number of the people present.