Vision-based People Counting for Attendance Monitoring System

Le, Manh Cuong; Le, My-Ha; Duong, Minh-Thien

doi:10.1109/gtsd50082.2020.9303117

Cited by 20 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, in related research, different algorithms have been designed for counting people's flow in and out, left and right, and front and back with respect to the LOI. Manh Cuong Le et al [14] proposed a methodology comprising two consecutive stages. Firstly, a detection task is executed to identify individuals within the current frame, with the MobileNetv2-SSD deep learning architecture employed for detection purposes.…”

Section: People Counting Using the Loimentioning

confidence: 99%

Edge-Computing-Based People-Counting System for Elevators Using MobileNet–Single-Stage Object Detection

Shen,

Chu

2023

Future Internet

View full text Add to dashboard Cite

Existing elevator systems lack the ability to display the number of people waiting on each floor and inside the elevator. This causes an inconvenience as users cannot tell if they should wait or seek alternatives, leading to unnecessary time wastage. In this work, we adopted edge computing by running the MobileNet–Single-Stage Object Detection (SSD) algorithm on edge devices to recognize the number of people inside an elevator and waiting on each floor. To ensure the accuracy of people counting, we fine-tuned the SSD parameters, such as the recognition frequency and confidence thresholds, and utilized the line of interest (LOI) counting strategy for people counting. In our experiment, we deployed four NVIDIA Jetson Nano boards in a four-floor building as edge devices to count people when they entered specific areas. The counting results, such as the number of people waiting on each floor and inside the elevator, were provided to users through a web app. Our experimental results demonstrate that the proposed method achieved an average accuracy of 85% for people counting. Furthermore, when comparing it to sending all images back to a remote server for people counting, the execution time required for edge computing was shorter, without compromising the accuracy significantly.

show abstract

Section: People Counting Using the Loimentioning

confidence: 99%

Edge-Computing-Based People-Counting System for Elevators Using MobileNet–Single-Stage Object Detection

Shen,

Chu

2023

Future Internet

View full text Add to dashboard Cite

show abstract

“…Edge Computing is a network system that requires compute to be located as close to the source of data as possible in order to reduce latency and bandwidth used to send data or to understand it in another way, there are fewer processes running on the cloud and moving those processes to the source instead. Since bringing compute to the network edge reduces the communication distance between the client and the server [13].…”

Section: Edge Computingmentioning

confidence: 99%

Edge Computing Based on Raspberry PI for People Counting in Smart Office

Namee,

Kaewsaeng-On,

Polpinij

et al. 2023

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

This research presents a technique for counting the number of people in a smart office room when walking in and out of the door by relying on a normal quality webcam. This means that the camera is not high resolution. The picture is not clear and sharp. Therefore, the image must be counted to adjust the color first before being processed in other steps. The designed system uses OpenCV and Raspberry Pi to help and is Edge Computing processing by comparing images in front view and top view. The front view is accurate at 58.44% for entering the room and 30.15% accurate for leaving the room. However, it was clear that the top view image would be more accurate at 87.82% for walking into the room and 80.75% out of 130 tests for walking out of the room. This system can also be used for counting people in other situations, such as counting people in factories, counting workers on the job site and counting people at trade fairs, etc.

show abstract

“…Overlapping Cameras System [38] Gallery-Probe database This system was developed to count the people who occur simultaneously on two cameras located at different positions in overlapping areas. A trained head detector was used to detect the heads from the images taken from the two cameras and these pictures of the two cameras are passed to a Siamese network.…”

Section: People Counting Based On Head Detection and Reidentification Inmentioning

confidence: 99%

A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras

et al. 2021

View full text Add to dashboard Cite

Automatic head tracking and counting using depth imagery has various practical applications in security, logistics, queue management, space utilization and visitor counting. However, no currently available system can clearly distinguish between a human head and other objects in order to track and count people accurately. For this reason, we propose a novel system that can track people by monitoring their heads and shoulders in complex environments and also count the number of people entering and exiting the scene. Our system is split into six phases; at first, preprocessing is done by converting videos of a scene into frames and removing the background from the video frames. Second, heads are detected using Hough Circular Gradient Transform, and shoulders are detected by HOG based symmetry methods. Third, three robust features, namely, fused joint HOG-LBP, Energy based Point clouds and Fused intra-inter trajectories are extracted. Fourth, the Apriori-Association is implemented to select the best features. Fifth, deep learning is used for accurate people tracking. Finally, heads are counted using Cross-line judgment. The system was tested on three benchmark datasets: the PCDS dataset, the MICC people counting dataset and the GOTPD dataset and counting accuracy of 98.40%, 98%, and 99% respectively was achieved. Our system obtained remarkable results.

show abstract

Vision-based People Counting for Attendance Monitoring System

Cited by 20 publications

References 6 publications

Edge-Computing-Based People-Counting System for Elevators Using MobileNet–Single-Stage Object Detection

Edge-Computing-Based People-Counting System for Elevators Using MobileNet–Single-Stage Object Detection

Edge Computing Based on Raspberry PI for People Counting in Smart Office

A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras

Contact Info

Product

Resources

About