People counting is a usual problem in visual surveillance. An accurate and real-time estimation of people in a crowded place can provide valuable information. Here video is inputted and gives the average number of people as output. The video input is separated to number of frames and some processing steps are performed on background subtraction results to estimate the number of people in a complicated scene, which includes people who are moving only slightly. A Threshold absolute difference algorithm is used here for background subtraction method. The extracted foreground image's pixels count is calculated and gives as input to the neural network. In learning phase, the people count is calculated by manually for test dataset. It is tested with remaining test cases by adjusting weight parameters to obtain relative to the target result.

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Background Subtraction Neural Network People Counting