A People Counting System for Use in CCTV Cameras in Retail

Cruz, Meygen; Keh, Jefferson James U.; Deticio, Ramiel; Tan, Carl Vincent; Jose, John Anthony C.; Dadios, Elmer P.

doi:10.1109/hnicem51456.2020.9400048

Cited by 8 publications

(2 citation statements)

References 7 publications

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“…To assess the performance, metrics evaluation and analysis were done. Besides, the main findings of this paper were compared with the other methods from previous works [9]- [21]. The accuracy and percentage error was calculated by [19]:…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Therefore, this review will focus on the most cutting-edge approaches and previous related work related to vision-based systems. To count people, M. Cruz [9] applied deep learning algorithms such as You Only Look Once (YOLOv3) and DeepSORT for object detection and classification and used the Kalman filter for object tracking, using Python and OpenCV. The YOLOv3 algorithm is frequently used by M. Ahmad [11] and I. Ahmed [14] also used it to detect objects.…”

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confidence: 99%

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Development of Automated People Counting System using Object Detection and Tracking

Chee¹,

Mazlan

2023

Int. J. Onl. Eng.

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The emergence of automation in the current economic trend promotes the usage of computer vision systems in various applications. Counting people in a specified area or on the street can bring many benefits in terms of security and marketing. The people counting system is one of the applications that utilize the computer vision system to count people with higher reliability and accuracy. Thus, this project is to develop an offline automated people counting system based on captured video file input using MATLAB software and a notification system to update and send notifications about the number of occupants in a target area using ThingSpeak. For project development, simulation and development of coding for object detection that involves deep learning approach, object tracking and counting, and development of notification system have been done. Three videos were taken to be used for three trials to evaluate the functionality and performance of the developed system. Based on the results and analysis, the system can perform people detection, people tracking and people counting on the recorded input videos with high accuracy of 94.45%, visualize the data on the ThingSpeak platform and send notifications through Twitter.

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Section: Performance Evaluationmentioning

confidence: 99%

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confidence: 99%