Image Analytics for Train Crowd Estimation

Abstract

Overcrowding is a common problem faced by train commuters in many countries. While waiting for the train at the stations, commuters tend to cluster and queue at doors that are closest to escalators and elevators that lead towards the station entrances and exits. This scenario results in trains not being fully utilized in terms of their capacity. As cabins with certain door positions tend to be more crowded than the rest of the cabins. The objective of this paper is to provide a methodology to estimate the crowd density within cabins of incoming trains, while leveraging on the existing train CCTV infrastructures. Providing the train cabin density information to commuters who are waiting for the incoming train allows the commuters to better select which cabin to board based on the provided density information. This will facilitate a better commuting experience without incurring a high cost for the train operator. To achieve this objective, we have adopted the usage of deep convolutional neural networks to analyze the footage from the existing security camera inside the trains and classify the images frames based the crowd level of train cabins. Three different experiments were conducted to train and test different convolutional neural network models. All models are able to make classification with an accuracy rate of over 90%.