Development of a Block-Based Real-Time People Counting System
In this paper, we propose a block-based real-time people counting system that can be used in various environments including shopping mall entrances, elevators and escalators. The main contributions of this paper are robust background subtraction, the block-based decision method and real-time processing. For robust background subtraction obtained from a number of image sequences, we used a mixture of K Gaussian. The block-based decision method was used to determine the size of the given objects (moving people) in each block. We divided the images into 72 blocks and trained the mean and variance values of the specific objects in each block. This was done in order to provide real-time processing for up to 4 channels. Finally, we analyzed various actions that can occur with moving people in real world environments.
KeywordsTraining Image Shadow Region Morphological Process Counting Line Counting Crowd
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