Passenger detection and counting for public transport system

Implementing accurate and reliable passenger detection and counting system is an important task for the correct distribution of available transport system. The aim of this paper is to develop an accurate computer vision-based system to track and count passengers. The proposed passenger detection system incorporates the ideas of well-established detection techniques and is optimally customised for both indoor and outdoor scenarios. The candidate foreground regions (inside an image) are extracted in the proposed method and are described using the histograms of oriented gradient descriptor. These features are trained and tested using support vector machine classifier and the detected passengers are tracked using a filter. The proposed counting system is used to count passengers automatically when they pass through a virtual line of interest. Accuracies ranging 91.2 percent to 86.24 percent were found for passenger detection using the proposed passenger detection and counting system whereas relative counting errors varied ten percent to thirteen percent.