Implementing Real-time Visitor Counter Using Surveillance Video and MobileNet-SSD Object Detection: The Best Practice

被引:0
作者
Al Musalhi, Nasser [1 ]
Al Wahaibi, Ali Mohammed [2 ]
Abbas, Mohammed [3 ]
机构
[1] Univ Technol & Appl Sci, Dept Informat Technol, Ibra, Oman
[2] Univ Selangor UNISEL, Fac Commun Visual Art & Comp, Bestari Jaya, Malaysia
[3] Univ Technol & Appl Sci, Dept Informat Technol, Sur, Oman
关键词
CNN; Mobilenet-SSD; MobileNet; Object tracking; real-time Object detection; SSD;
D O I
10.21123/bsj.2024.10540
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页码:1775 / 1785
页数:11
相关论文
共 49 条
[41]   Develop a novel deep learning-based framework using convolutional neural networks for real-time object detection and tracking in embedded systems [J].
Zhang, Kai .
JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING, 2025,
[42]   Diminished reality system with real-time object detection using deep learning for onsite landscape simulation during redevelopment [J].
Kido, Daiki ;
Fukuda, Tomohiro ;
Yabuki, Nobuyoshi .
ENVIRONMENTAL MODELLING & SOFTWARE, 2020, 131
[43]   Real-time robust object detection using an adjacent feature fusion-based single shot multibox detector [J].
Kim D. ;
Park S. ;
Kang D. ;
Paik J. .
IEIE Transactions on Smart Processing and Computing, 2020, 9 (01) :22-27
[44]   Development of real-time screening system for structural surface damage using object detection and generative model based on deep learning [J].
Nomura Y. ;
Shigemura K. .
Zairyo/Journal of the Society of Materials Science, Japan, 2019, 68 (03) :250-257
[45]   Deep SCNN-Based Real-Time Object Detection for Self-Driving Vehicles Using LiDAR Temporal Data [J].
Zhou, Shibo ;
Chen, Ying ;
Li, Xiaohua ;
Sanyal, Arindam .
IEEE ACCESS, 2020, 8 :76903-76912
[46]   Performance comparison of CNN, QNN and BNN deep neural networks for real-time object detection using ZYNQ FPGA node [J].
Mani, V. R. S. ;
Saravanaselvan, A. ;
Arumugam, N. .
MICROELECTRONICS JOURNAL, 2022, 119
[47]   Real-Time Garbage Object Detection With Data Augmentation and Feature Fusion Using SUAV Low-Altitude Remote Sensing Images [J].
Chen, Weiyang ;
Wang, Haifeng ;
Li, Hao ;
Li, Quanjing ;
Yang, Yang ;
Yang, Kun .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19
[48]   VIRTUALOT - A FRAMEWORK ENABLING REAL-TIME COORDINATE TRANSFORMATION & OCCLUSION SENSITIVE TRACKING USING UAS PRODUCTS, DEEP LEARNING OBJECT DETECTION & TRADITIONAL OBJECT TRACKING TECHNIQUES [J].
Koskowich, Bradley J. ;
Rahnemoonfar, Maryam ;
Starek, Michael .
IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2018, :6416-6419
[49]   RTF-RCNN: An Architecture for Real-Time Tomato Plant Leaf Diseases Detection in Video Streaming Using Faster-RCNN [J].
Alruwaili, Madallah ;
Siddiqi, Muhammad Hameed ;
Khan, Asfandyar ;
Azad, Mohammad ;
Khan, Abdullah ;
Alanazi, Saad .
BIOENGINEERING-BASEL, 2022, 9 (10)