Implementing Real-time Visitor Counter Using Surveillance Video and MobileNet-SSD Object Detection: The Best Practice
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Abstract
Counters that keep track of the number of people who enter a building are a useful management tool for keeping everyone who uses it safe and happy. This paper aims to employ the MobileNet-SSD machine learning approach to implement a best practice for visitor counter. The researchers have to build a different scenario test dataset along with the MOT20 dataset to achieve the proposed methodology. Implementing different experiments in single-user, one-one; two-two users; many-two, and multiple users in different walking directions to detect and count shows varied results based on the experiment type. The best achieved by single-user and one-to-one model; both are scored 100% of detecting and calculating for in or out.
Received 28/12/2023
Revised 19/04/2024
Accepted 21/04/2024
Published 25/05/2024
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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References
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