Abstract
The Covid 19 pandemic has brought challenges to transportation as social distancing measures were hard to implement on overcrowded public vehicles. This paper presents an LRT passenger management system using image processing algorithms with a Raspberry Pi and multiple cameras. This works by counting and limiting the passengers inside the train. The model also includes an early warning system and an alarm to alert passengers when the vehicle has passed a particular threshold limit. After testing on different scenarios, the system obtained 96.5% accuracy on single entry and exit of passengers and 1 to 2 s to count. Meanwhile, the results have shown 88.22% for simultaneous entry and 93% for simultaneous exits. For simultaneous entry and exits, the system has obtained 94.44%. Overall, this study was able to develop a low-cost passenger management system with high accuracy and fast time to count. Moreover, we were able to determine the parameters to vary to improve the overall accuracy of the system.
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Paulme, D.M., Perez, M., Sese, J. (2021). Light Rail Transit (LRT) Passenger Management System Using Image Processing Algorithm with Raspberry Pi and Multiple Cameras. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Software Engineering Application in Informatics. CoMeSySo 2021. Lecture Notes in Networks and Systems, vol 232. Springer, Cham. https://doi.org/10.1007/978-3-030-90318-3_57
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