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Journals / Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering / 2024 / Volume: 66 Issue: 2

A comparison of machine learning methods for queue length detection

Mehmet Eren Yeşilyurt(KASTAMONU ÜNİVERSİTESİ)0000-0002-7322-5572
Mehmet Serdar Güzel(ANKARA ÜNİVERSİTESİ)0000-0002-3408-0083
Ebru Sezer(HACETTEPE ÜNİVERSİTESİ)0000-0002-9287-2679
Pages
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Publication Date
2024-12-11
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results show that the method using RoiAlign presents the best prediction performance in terms of mean squared error and mean absolute error, compared to other methods.

📌 3 Key Takeaways

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Objective & Hypothesis:Queues are formed by people waiting for a service in public institutions and they can be defined as orderly groups of people.
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Key Findings & Data:Automatically counting the number of people waiting in a queue through video camera footage would provide these institutions with valuable information with regards to customer service quality.
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Conclusion & Impact:Results show that the method using RoiAlign presents the best prediction performance in terms of mean squared error and mean absolute error, compared to other methods.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Queue length detection · crowd counting · machine learning

Abstract

Queues are formed by people waiting for a service in public institutions and they can be defined as orderly groups of people. Automatically counting the number of people waiting in a queue through video camera footage would provide these institutions with valuable information with regards to customer service quality. In this paper, our goal is to compare several machine learning methods for finding the total number of people waiting in a queue given video camera frames. We approached this problem as a regression task. We used a subset of the Collective Activity Dataset and compared three different methods. The first two methods used bounding box coordinates and orientations provided by the dataset, while the last method utilized the bounding box coordinates to extract feature maps from the frames using RoiAlign. The first method used XGBoost, while the latter methods used Convolutional Neural Networks (CNNs). Results show that the method using RoiAlign presents the best prediction performance in terms of mean squared error and mean absolute error, compared to other methods.

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Cite This Article

Yeşilyurt, M. E., Güzel, M. S., Sezer, E. (2024). A comparison of machine learning methods for queue length detection. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, Volume: 66 Issue: 2. https://doi.org/10.33769/aupse.1415447

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