Congestion Control early warning system using Deep Learning
A new approach is proposed to analyze the live crowd and to provide an alert at the time of congestion, over-crowding and sudden gathering of pedestrians in a particular region. This paper proposes a completely software-oriented approach using MATLAB where it uses object detection and object tracking using Faster R- CNN (Region Based Convolutional Neural Network) algorithm where inception model of Google is used as CNN model which is pre-trained. This proposed method gives significant result on proposed dataset and the crowd congestion using Faster R-CNN approach which gives an accuracy of 93.503% at the rate 28 frames per second and the crowd detected video frames are uploaded to cloud storage.
Victor Hugo Rold, Reis, Silvio Jamil F. Guimar, Zenilton Kleber Goncalves do Patroc, (2020). Dense Crowd Counting with Capsule Networks, 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), IEEE, Brazil. https://doi.org/10.1109/IWSSIP48289.2020.9145163
Pradeepa B., Viji A., Joshan Athanesious J., Vaidehi V., (2020). Anomaly Detection in Crowd Scenes usingStreak Flow Analysis, 2019 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), IEEE, India. https://doi.org/10.1109/WiSPNET45539.2019.9032845
Mahdyar Ravanbakhsh, Moin Nabi, Hossein Mousavi, Enver Sangineto & Nicu Sebe, (2018). Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, USA. https://doi.org/10.1109/WACV.2018.00188
Yuanyuan Fan & Qingzhong Liang, (2017). An Improved Method for Detection of The Pedestrian Flow Based on RFID, 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), IEEE, China. https://doi.org/10.1109/CSE-EUC.2017.23
Jonathan Huang, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song, Sergio Guadarrama, Kevin Murphy, (2007). Speed/accuracy trade-offs for modern convolutional object detectors, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), USA. https://doi.org/10.1109/CVPR.2017.351
Shangnan Liu, Qiang Cheng, Zhenjiang Zhu, & Hao Zhang, (2016). Analysis and Design of Public Places Crowd Stampede Early- Warning Simulating System, 2016 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII), China. https://doi.org/10.1109/ICIICII.2016.0058
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg, (2016). Ssd: Single shot multibox detector, European conference on computer vision, Springer, 21-31. https://doi.org/10.1007/978-3-319-46448-0_2
Pathan S., Al-Hamadi A., & Michaelis B., (2010). Crowd behavior detection by statistical modeling of motion patterns, 2010 International Conference of Soft Computing and Pattern Recognition, IEEE, France. https://doi.org/10.1109/SOCPAR.2010.5686403
N. Farooqi, Intelligent safety management system for crowds using sensors, 2017 12th International Conference for Internet Technology and Secured Transactions (ICITST), IEEE, Cambridge. https://doi.org/10.23919/ICITST.2017.8356365
Vidyasagaran S., Devi S.R., Varma A., Rajesh A., Charan H., (2017). A low cost IoT based crowd management system for public transport, 2017 International Conference on Inventive Computing and Informatics (ICICI), IEEE, India. https://doi.org/10.1109/ICICI.2017.8365342
Rohit K., Mistree K., Lavji J., (2017). A review on abnormal crowd behavior detection, 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), IEEE, India. https://doi.org/10.1109/ICIIECS.2017.8275999
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