Abstract
Increased urbanization and on-demand mobility have resulted in the boon of many ride-sharing companies. Besides providing faster, economical, and comfortable rides, these rideshares are also environment-friendly as they save a lot of energy. This research work presents a model which would be beneficial for the passengers riding these carpools as it would not only help to curb the spread of infection in current pandemic but also detect whether the driver is drowsy or not to prevent possible road accidents. The proposed web application includes three detections based on novel deep learning algorithms implementing face recognition, facemask, and drowsiness detection of the driver with an alert mechanism to send immediate email alerts to the company and driver. The novelty of the proposed application is that the current and live status of the driver is continuously recorded using latest technologies like convolutional neural networks (CNNs), histogram of oriented gradients (HOG), support vector machine (SVM) classifier, and computer vision. In addition to this, a real-time vehicle tracking device is also implemented using Node MCU, Global Positioning System (GPS) module, and Blynk app to keep the company updated about the real-time location of the rideshare. The name of the recognized driver is displayed as output. Face mask and drowsiness detection is done with an accuracy of 99%, and the real-time location of the cab is indicated in Google Maps on the Blynk app. The proposed web application would be very beneficial for ride-sharing companies in the current COVID situation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Diaz YM (2021) Benchmarking lightweight face architectures on specific face recognition scenarios. Springer. https://doi.org/10.1007/s10462-021-09974-2
Fredj HB (2020) Face recognition in unconstrained environment with CNN. Springer. https://doi.org/10.1007/s00371-020-01794-9
Loey M (2020) A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic. ScienceDirect. https://doi.org/10.1016/j.measurement.2020.108288
Nieto-Rodriguez A (2015) System for medical mask detection in the operating room through facial attributes. Springer. https://doi.org/10.1007/978-3-319-19390-8_16
Aulia S (2021) Comparison of different classifiers for drowsiness detection based on facial expression recognition. Springer. http://jonuns.com/index.php/journal/article/view/494
Galarza EE (2018) Real time driver drowsiness detection based on driver’s face image behaviour using a system of human computer interaction implemented in a smartphone. In: International conference on information technology and systems. Springer. https://doi.org/10.1007/978-3-319-73450-7_53
Adardour HE (2020) Outdoor Alzheimer’s patients tracking using an IoT system and Kalman filter estimator. Springer. https://doi.org/10.1007/s11277-020-07713-4
Pranav KB, Manikandan J (2020) Design and evaluation of a real time face recognition system using convolutional neural networks. In: Third international conference on computing and network communications. ScienceDirect
Büyüktaş B (2020) Curriculum learning for face recognition. In: 2020 28th European signal processing conference (EUSIPCO). IEEE Explore. https://doi.org/10.23919/Eusipco47968.2020.9287639
Muthazhagan B. Ameliorated face and iris recognition using deep convolutional networks. Springer. https://doi.org/10.1007/978-3-030-57024-8
Sarkar SD (2020) Face recognition using artificial neural network and feature extraction. In: 2020 7th international conference on signal processing and integrated networks (SPIN). https://doi.org/10.1109/SPIN48934.2020.9071378
Militante SV (2020) Real-time facemask recognition with alarm system using deep learning. In: 2020 11th IEEE control and system graduate research colloquium. https://doi.org/10.1109/ICSGRC49013.2020.9232610
Pooja S (2021) Face mask detection using AI. Springer. https://doi.org/10.1007/978-981-33-4236-1_16
Mehta S (2019) Real-time driver drowsiness detection system using eye aspect ratio and eye closure ratio. In: International conference on sustainable computing in science, technology and management
Venkata Subbaiah D (2021) A novel approach for detection of driver drowsiness using behavioural measures. Springer. https://doi.org/10.1007/978-981-15-5397-4_41
Sreelakshmi KK (2020) A non-invasive approach for driver drowsiness detection using convolutional neural networks. Springer. https://doi.org/10.1007/978-981-15-5788-0_13
Almomani IM (2011) Ubiquitous GPS vehicle tracking and management system. IEEE. https://doi.org/10.1109/AEECT.2011.6132526
Liu Q (2006) Research and design of intelligent vehicle monitoring system based on GPS/GSM. In: 2006 6th international conference on ITS telecommunications. https://doi.org/10.1109/ITST.2006.288858
Mangla N (2017) A GPS-GSM predicated vehicle tracking system, monitored in a mobile app based on Google Maps. In: 2017 international conference on energy, communication, data analytics and soft computing (ICECDS). IEEE. https://doi.org/10.1109/ICECDS.2017.8389989
Conflict of Interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rakshit, T., Shrestha, A., Chhabra, S., Jindal, S.K. (2023). Experimental Implementation of COVID-19 Safety Measures in Ride-Sharing Cabs Using Deep Learning and Internet of Things. In: Rawat, S., Kumar, S., Kumar, P., Anguera, J. (eds) Proceedings of Second International Conference on Computational Electronics for Wireless Communications. Lecture Notes in Networks and Systems, vol 554. Springer, Singapore. https://doi.org/10.1007/978-981-19-6661-3_21
Download citation
DOI: https://doi.org/10.1007/978-981-19-6661-3_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-6660-6
Online ISBN: 978-981-19-6661-3
eBook Packages: EngineeringEngineering (R0)