Passenger Counting in Mass Public Transport Systems using Computer Vision and Deep Learning

Authors

Keywords:

Estimate counting, station door images, density maps, neural networks, computer vision.

Abstract

Estimating the number of people in vehicles and stations in public transport systems is crucial to improve service quality. The TransMilenio system in Bogotá has serious drawbacks due to the lack of information in congestion situations. In this work we present a computer vision method that estimates the number of people in TransMilenio stations using deep learning techniques. We offer free use of the TransMilenio-Javeriana database with nearly 900,000 head labels on buses and stations. From these images a deep learning architecture tuned for crowd counting was trained to generate density maps around the heads in the scene. Several head count methods were evaluated on the density maps. After testing the method with 10,800 images, the results show a mean absolute error of 1 head per frame, equivalent to 11% relative error. The accuracy of this method is much better than its manual counterpart. This method is also scalable and low cost, which indicates that it has great potential to provide information for the planning and operation of public transport systems.

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Author Biographies

William David Moreno Rendon, Pontificia Universidad Javeriana

Electronic Engineer from Pontificia Universidad Javeriana (Bogotá, Colombia), with academic experience in artificial intelligence, signal processing and design of software and hardware solutions. He has worked as a cybersecurity analyst in the IT Security area at BTG Pactual Colombia. He is currently a researcher at the Department of Electrical and Computer Engineering at the University of Delaware, U

Carolina Burgos Anillo, Pontificia Universidad Javeriana, Bogotá, Colombia

Electronic Engineer from Pontificia Universidad Javeriana (Bogotá, Colombia), with academic experience in artificial intelligence techniques and the design of hardware and software solutions. Since January 2022 she has been working in software engineering, focusing mainly on web development.

Daniel Jaramillo-Ramirez, Pontificia Universidad Javeriana, Bogotá, Colombia

Electronic Engineer (UPB Medellin 2006), Master in Electronics (Uniandes Bogota 2008) and Ph.D. in Telecommunications (Supélec Gif-sur-Yvette, 2014). He has worked for Orange Labs (Paris) in research for 3GPP RAN1 standardization. Since 2014 he is an Assistant Professor in the Department of Electronics at Pontificia Universidad Javeriana in Bogota and is an active researcher on wireless communications, and urban transport, especially in quality of service for public transport systems and electric bicycles.

Henry Carrillo, Pontificia Universidad Javeriana, Bogotá, Colombia

MSc. and Ph.D. in Computer Science and Systems Engineering from the University of Zaragoza (Zaragoza, Spain), Master in Electronic Engineering from the Pontificia Universidad Javeriana (Bogotá, Colombia) and Electronic Engineer from the Universidad del Norte (Barranquilla, Colombia), with experience in artificial intelligence techniques, the design of electronic hardware and algorithms for autonomous systems, including computer vision systems, mobile robotic systems, embedded systems, and intelligent systems.

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Published

2023-03-23

How to Cite

Moreno Rendon, W. D., Burgos Anillo, C. ., Jaramillo-Ramirez, D., & Carrillo, H. (2023). Passenger Counting in Mass Public Transport Systems using Computer Vision and Deep Learning. IEEE Latin America Transactions, 21(4), 537–545. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/7331