Anovel framework for automatic passenger counting

We propose a novel framework for counting passengers in a railway station. The framework has three components: people detection, tracking and validation. We detect every person using Hough circle when he or she enters the field of view. The person is then tracked using optical flow until (s)he leaves the field of view. Finally, the tracker generated trajectory is validated through a spatiotemporal background subtraction technique. The number of valid trajectories provides passenger count. Each of the three components of the proposed framework has been compared with competitive methods on three datasets of varying crowd densities. Extensive experiments have been conducted on the datasets having top views of the passengers. Experimental results demonstrate that the proposed algorithmic framework performs well both on dense and sparse crowds and it can successfully detect and track persons with different hair colors, hoodies, caps, long winter jackets, bags and so on. The proposed algorithm shows promising results also for people moving in different directions. The proposed framework can detect up to 30% more accurately and 20% more precisely than other competitive methods.