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Journal articles on the topic 'Eyes Detection and Tracking'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

Jadhav, Suraj, Sanket Jagdale, Niraj Jangale, and Shital Raut. "Driver Drowsiness Detection System using Raspberry Pi." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 1497–501. http://dx.doi.org/10.22214/ijraset.2022.48137.

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Abstract: This paper presents a real-time driver drowsiness detection system for driving safety. Supported computer vision techniques, the driver’s face is found from color video captured in an exceedingly car. Then, face detection is used to locate the regions of the driver’s eyes, which are used because the templates for eye tracking in subsequently frames. Finally, the tracked eye’s images are used for drowsiness detection so as to come up with waring alarms. The proposed approach hasthree phases: Face, Eye detection and drowsiness detection The role of image processing is to acknowledge th

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Băiașu, Ana-Maria, and Cătălin Dumitrescu. "Contributions to Driver Fatigue Detection Based on Eye-tracking." International Journal of Circuits, Systems and Signal Processing 15 (January 18, 2021): 1–7. http://dx.doi.org/10.46300/9106.2021.15.1.

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In recent years, one of the most important factors in road accidents is the drowsiness of drivers and the distraction while driving. In this paper, we describe a system that monitors the detection of fatigue or drowsiness. The proposed solutions follow the driver's gaze, and if the system identifies the closed eyes, it triggers an alarm signal intended to alert against losing control of the car and causing traffic accidents. Eye-tracking is the process that measuring the eye position and eye movement. The proposed method is structured in three phases. In the first phase, eye images are capture

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B., Vijayalaxmi, Sekaran Kaushik, Neelima N., Chandana P., N. Meqdad Maytham, and Kadry Seifedine. "Implementation of face and eye detection on DM6437 board using simulink model." Bulletin of Electrical Engineering and Informatics 9, no. 2 (2020): 785–91. https://doi.org/10.11591/eei.v9i2.1703.

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Driver Assistance system is significant in drriver drowsiness to avoid on road accidents. The aim of this research work is to detect the position of driver’s eye for fatigue estimation. It is not unusual to see vehicles moving around even during the nights. In such circumstances there will be very high probability that a driver gets drowsy which may lead to fatal accidents. Providing a solution to this problem has become a motivating factor for this research, which aims at detecting driver fatigue. This research concentrates on locatingthe eye region failing which a warning signal is gen

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Mohammed, Anes J., and Dr.A.R.JayaSudha. "Driver Drowsiness Detection System." Advanced Innovations in Computer Programming Languages 5, no. 2 (2023): 8–15. https://doi.org/10.5281/zenodo.8037360.

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<em>Drowsy drivers cause several accidents every year. It's a major contributor to vehicular mishaps in the modern era. According to recent data, driver fatigue is a leading cause of accidents. Thousands of people lose their lives every year in vehicle accidents brought on by sleepy drivers. Drowsiness contributes to almost 30% of all accidents. A system that can detect driver fatigue and provide an alarm in time to avert an accident is essential. In this study, we provide a method for identifying sleepy drivers. In this system, the driver is constantly watched over by a camera. The driver

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Prabiantissa, Citra Nurina, Kurniawan Muchamad, and Achmad Fadlan Bhahreisy. "Deteksi Mata dan Alis Menggunakan Adaboost Classifier dan Haar Cascade." JURNAL FASILKOM 14, no. 3 (2024): 705–14. https://doi.org/10.37859/jf.v14i3.7394.

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Since COVID-19 pandemic, using a mask is a daily necessity. Massive use of masks poses challenges to face recognition, surveillance cameras, age estimation, eye gaze tracking systems, and driver fatigue monitoring systems based on face detection. From these problems, a study is needed to be able to detect the eyes and eyebrows on faces that use masks. This study aims to implement eye and eyebrow detection using the Haar Cascade method. This research went through several processes, including pre-processing, integral image, training and testing using the Haar Cascade method. The results of the s

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Vijayalaxmi, B., Kaushik Sekaran, N. Neelima, P. Chandana, Maytham N. Meqdad, and Seifedine Kadry. "Implementation of face and eye detection on DM6437 board using simulink model." Bulletin of Electrical Engineering and Informatics 9, no. 2 (2020): 785–91. http://dx.doi.org/10.11591/eei.v9i2.1703.

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Driver Assistance system is significant in drriver drowsiness to avoid on road accidents. The aim of this research work is to detect the position of driver’s eye for fatigue estimation. It is not unusual to see vehicles moving around even during the nights. In such circumstances there will be very high probability that a driver gets drowsy which may lead to fatal accidents. Providing a solution to this problem has become a motivating factor for this research, which aims at detecting driver fatigue. This research concentrates on locating the eye region failing which a warning signal is generate

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Venkatapathi, Pallam, Kondu Vinay, Kummathi Harshavardhan Reddy, Mulli Karthik, and Dr Sudhakar Alluri. "Real Time Driver Gaze Tracking and Eyes off the Road Detection System." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 1906–11. http://dx.doi.org/10.22214/ijraset.2023.56316.

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Abstract: Driver Gaze Tracking and Eyes Off the Road Detection System is a technology-driven solution designed to enhance road safety by monitoring driver behavior and detecting instances when the driver's attention is diverted from the road. The system utilizes computer vision techniques and machine learning algorithms to track the driver's eye movements and identify potential distractions. By providing real-time alerts and warnings, this system aims to mitigate the risk of accidents caused by distracted driving. Distracted driving poses a significant risk to road safety, and traditional meth

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H, Ritish. "Real Time Driver Drowsiness Detection System using OpenCV." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 3254–60. http://dx.doi.org/10.22214/ijraset.2021.35811.

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Most knowledge is transmitted by the eyes, an essential part of the body. When an operator is in a state of exhaustion, facial expressions, e.g., blinking and yawning rate, vary from those in normal condition. In this venture, we are proposing a system named Driver-Drowsiness Detection System, which monitors the exhaustion state of the drivers, such as yawning, and eye closing length, using video clips, without equipping their bodies with sensors. We are using face-tracking algorithm to improve tracking reliability due to the limitations of previous algorithms. We have used facial region detec

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Kang, Dongwoo, and Jingu Heo. "Content-Aware Eye Tracking for Autostereoscopic 3D Display." Sensors 20, no. 17 (2020): 4787. http://dx.doi.org/10.3390/s20174787.

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This study develops an eye tracking method for autostereoscopic three-dimensional (3D) display systems for use in various environments. The eye tracking-based autostereoscopic 3D display provides low crosstalk and high-resolution 3D image experience seamlessly without 3D eyeglasses by overcoming the viewing position restriction. However, accurate and fast eye position detection and tracking are still challenging, owing to the various light conditions, camera control, thick eyeglasses, eyeglass sunlight reflection, and limited system resources. This study presents a robust, automated algorithm

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Singh, Priyanshu, Vishal Kumar Singh, and Vishal Verma. "Drowsy Driver Detection using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 2431–38. http://dx.doi.org/10.22214/ijraset.2023.52135.

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Abstract: The face is an essential body feature that reveals a lot of information. When a driver is fatigued, their facial expressions change from what they would be in a normal state, such as their eyes blinking more frequently than usual. In this study, we used CNN algorithm to recognize signs of driver drowsiness such as blinking and length of eye closure. CNN algorithm uses video pictures. Due to the drawbacks of existing techniques, we propose a unique face-tracking algorithm to enhance tracking accuracy. Based on human key characteristics, we applied a unique detection algorithm i.e. CNN

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Kanakambika, N. L. Preethi, Priyanka, S. Varekar Shraddha, and B. C. Anil. "An Optimised Eye Blink Detection Mechanism for Disabled Persons." Journal of Signal Processing 5, no. 3 (2019): 23–29. https://doi.org/10.5281/zenodo.3540429.

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<em>By this project, we explain an actual technique using some image and video meting out algorithms for eye blink detection. The object of this technique is that help to disabled those who cannot communicate with humans. In this we use the Haar cascade algorithm to detect the angle of eye and face for the information on the eyes and the facemask axis. To include with this, the similar classifier created on Haar's characteristics is used to discover the interaction between the eyes and the facial axis to position them. To detect the position of the observed face the effective ocular tracki

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K. Avinash Babu and G Eswara Rao. "Driver Drowsiness Detection System for Accident Prevention." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 12 (2024): 2696–702. https://doi.org/10.47392/irjaeh.2024.0372.

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The abstract highlights the importance of driver drowsiness detection as a critical component of vehicle safety technology, with the goal of preventing accidents brought on by drowsy drivers. According to studies, driver fatigue possibly a factor in 20% of traffic accidents, underscoring the importance of developing efficient accident-avoidance strategies. The research focuses on a particular illustration of an automated tiredness detection system intended to improve driver safety by tracking unsafe driving practices. The primary objective of the research is to develop an automated system capa

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Lagunes-Ramírez, Derick Axel, Gabriel González-Serna, Leonor Rivera-Rivera, Nimrod González-Franco, María Y. Hernández-Pérez, and José A. Reyes-Ortiz. "Through the Youth Eyes: Training Depression Detection Algorithms with Eye Tracking Data." IEEE Latin America Transactions 23, no. 1 (2025): 6–16. https://doi.org/10.1109/tla.2025.10810399.

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Kuo, Yung-Lung, Jiann-Shu Lee, and Min-Chai Hsieh. "Video-Based Eye Tracking to Detect the Attention Shift." International Journal of Distance Education Technologies 12, no. 4 (2014): 66–81. http://dx.doi.org/10.4018/ijdet.2014100105.

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Eye and head movements evoked in response to obvious visual attention shifts. However, there has been little progress on the causes of absent-mindedness so far. The paper proposes an attention awareness system that captures the conditions regarding the interaction of eye gaze and head pose under various attentional switching in computer classroom. Via the algorithm of complexion area detection, eye location and eye tracking, the system detects the shifts of the subject's attention, records it and sends a notification to the class teacher. In five variant experiments of attentional shift, the a

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Ashwini Sonawane, Nikita Deore, Rohan Sonawane, Prof. A. S. Nalge, and Prof. N. V. kapade. "Eye Based Communication System for Speech Disability People." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 03 (2025): 1012–16. https://doi.org/10.47392/irjaem.2025.0165.

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Eye-based communication languages such as Blink-To-Speak play a key role in expressing the needs and emotions of patients with motor neuron disorders. Most invented eye-based tracking systems are complex and not affordable in low-income countries. Blink-To-Live is an eye-tracking system based on a modified Blink-To-Speak language and computer vision for patients with speech impairments. A laptop camera tracks the patient’s eyes by sending real-time video frames to computer vision modules for facial landmarks detection, eye identification and tracking. There are four defined key alphabets in th

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Satar, Mohammed, Basim Alshammari, and Hayder Jasim. "Eye Movement Tracking Using Opencv Python." Wasit Journal of Engineering Sciences 11, no. 2 (2023): 71–81. http://dx.doi.org/10.31185/ejuow.vol11.iss2.393.

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In this study, we made a simple, low-cost algorithm for tracking eye movements and eye blinks in real-time and non-real-time. Several methods are being used right now. Show parts of the face, like the eyes or the whole face. For this reason, open-source libraries like OpenCV enable high-level programming to implement reliable and accurate detection algorithms like Haar Cascade. Since everything is processed in real-time, payment must be made quickly. Pay attention to how hardware, like a computer, can only use a certain amount of resources (processing power). The system has been proven to work

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Azimi Sotudeh, Mohammad Ali, Hasan Ziafat, and Said Ghafari. "Pupil Detection in Facial Images with Using Bag of Pixels." Advanced Materials Research 468-471 (February 2012): 2941–48. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.2941.

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To detect and track eye images, distinctive features of user eye are used. Generally, an eye-tracking and detection system can be divided into four steps: Face detection, eye region detection, pupil detection and eye tracking. To find the position of pupil, first, face region must be separated from the rest of the image using bag of pixels, this will cause the images background to be non effective in our next steps. We used from horizontal projection, to separate a region containing eyes and eyebrow. This will result in decreasing the computational complexity and ignoring some factors such as

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Kawato, Shinjiro, and Nobuji Tetsutani. "Detection and tracking of eyes for gaze-camera control." Image and Vision Computing 22, no. 12 (2004): 1031–38. http://dx.doi.org/10.1016/j.imavis.2004.03.013.

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Shiva Kumar Kamble, Rahul Jena, Uttej Reddy Orra, and Mohammad Haseeb Khan. "A survey on drowsiness detection system with advanced face tracking." World Journal of Advanced Research and Reviews 21, no. 3 (2024): 1748–53. http://dx.doi.org/10.30574/wjarr.2024.21.3.0809.

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To address the increasing dangers associated with driver and worker fatigue, this project introduces an advanced Drowsiness Detection System featuring state-of-the-art face-tracking capabilities. The pressing need for fatigue detection is evident in the alarming figures of 800 annual fatalities and 50,000 injuries resulting from drowsy driving incidents. This research expands the application of the technology to industrial workplaces, where the consequences of drowsiness are equally severe. Our comprehensive approach involves real-time monitoring of facial features, with a focus on eye movemen

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Shiva, Kumar Kamble, Jena Rahul, Reddy Orra Uttej, and Haseeb Khan Mohammad. "A survey on drowsiness detection system with advanced face tracking." World Journal of Advanced Research and Reviews 21, no. 3 (2024): 1748–53. https://doi.org/10.5281/zenodo.14147949.

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To address the increasing dangers associated with driver and worker fatigue, this project introduces an advanced Drowsiness Detection System featuring state-of-the-art face-tracking capabilities. The pressing need for fatigue detection is evident in the alarming figures of 800 annual fatalities and 50,000 injuries resulting from drowsy driving incidents. This research expands the application of the technology to industrial workplaces, where the consequences of drowsiness are equally severe. Our comprehensive approach involves real-time monitoring of facial features, with a focus on eye movemen

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Aanchal Takkar, Sumer Yadav, Radhika Gupta, Swati Sah,. "Project Awakesure: Intelligent Drowsiness Detection Using Eye Tracking." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11 (2024): 906–14. http://dx.doi.org/10.17762/ijritcc.v11i11.10362.

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Being sleepy or drowsy is referred to as being drowsy. A person who is sleepy may feel exhausted or lethargic and struggle to stay awake. People who are sleepy tend to be less attentive and may even nod off, though they can still be awakened. An increasing number of vocations nowadays call for sustained focus. In order for drivers to respond quickly to unexpected incidents, they must maintain a watchful eye on the road. Many road incidents are directly caused by tired drivers. In order to drastically lower the frequency of fatigue-related auto accidents, it is crucial to develop technologies t

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Xu, Meng Xi, Xin Wang, Xi Jun Yan, Guo Fang Lv, Sheng Nan Zheng, and Hui Bin Wang. "Polarization Imaging Target Detection Method by Imitating Dragonfly Compound Eye LF-SF Mechanism." Applied Mechanics and Materials 347-350 (August 2013): 3881–84. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3881.

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Recently, water surface target detection and tracking for sea, lake, or river are challenging research topics. This paper presents a framework of target detection and tracing based on three-channel synchronization polarization imaging and imitation dragonfly compound eye LF-SF (large field-small field) mechanism. This framework can make full use of the advantages of polarization sensitivity of the compound eyes of a dragonfly, and be useful for effective water surface target detection and motion vector estimation.

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K, Boomika. "EYE-BLINK DETECTION ASSISTIVE SYSTEM FOR PARALYZED PATIENT." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34720.

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Paralysis is defined as the complete loss of muscle function in any part of the body. It occurs when there is a problem with the passage of messages between the muscles and the brain. Some paralyzed people cannot move even a single part of the body other than their eyes. Hence, the main aim of this project is to design a real time interactive system that can assist the paralyzed to control appliances such as lights, fans or by playing pre-recorded audio messages, through a predefined number of eye blinks. Image processing techniques have been implemented in order to detect the eye blinks. In o

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R., Manikandan, Abilash S., Agilakalanchian C., and Tamilselvan P. "DRIVER DROWSINESS DETECTION SYSTEM USING OPEN COMPUTER VISION." International Journal of Current Research and Modern Education 3, no. 1 (2018): 410–14. https://doi.org/10.5281/zenodo.1218681.

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In recent years driver fatigue is one of the major causes of vehicle accidents in the world. A direct way of measuring driver fatigue is measuring the state of the driver i.e. drowsiness.  So it is very important to detect the drowsiness of the driver to save life and property. This project is aimed towards developing a prototype of drowsiness detection system. This system is a real time system which captures image continuously and measures the state of the eye according to the specified algorithm and gives warning if required. Though there are several methods for measuring the drowsiness

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S. B, Prof Hiranawale. "Vehicle Counting System." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 1244–47. http://dx.doi.org/10.22214/ijraset.2022.47488.

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Summary: Object detection is a computer technology related to computer vision and image processing concerned with detecting instances of semantic objects of a certain class in digital images and videos. Machine learning can be used to detect and classify objects in images and videos. Vehicle detection, also known as computer object recognition, is essentially scientific methods and means of seeing machines, not human eyes. Vehicle detection is one of the features most used by businesses and organizations today. We can use computer vision to detect different types of media on video or in real t

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Dr., Sakthivel. "Driver Recognition and Drowsiness Detection using Deep Learning Technique." International Research Journal of Computer Science 10, no. 06 (2023): 302–6. http://dx.doi.org/10.26562/irjcs.2023.v1006.05.

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Drowsiness and fatigue of automobile drivers reduce the drivers’ abilities of car manage, herbal reflex, recognition and notion. Such diminished vigilance stage of drivers is found at night time driving or overdriving, causing twist of fate and pose extreme danger to mankind and society. Therefore, it is very tons essential in this recent fashion in vehicle industry to include driving force help system which could hit upon drowsiness and fatigue of the drivers. This undertaking offers a nonintrusive prototype computer vision gadget for monitoring a driving force’s vigilance in real time. Eye t

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Barki, Archana M. "Eye Tracker for Password Authentication." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 1727–33. http://dx.doi.org/10.22214/ijraset.2021.35344.

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In today’s quickly expanding digital environment, security is one of the most important worries that everyone has. Personal Identification Numbers, or PINs, are used to address security issues. Password authentication with PINs, on the other hand, requires clients to physically enter the password, which can be cracked via heat monitoring or thermal tracking. Hands-off gaze-based password or PIN entering techniques used in password or PIN authentication leave no physical traces and so provide the highest level of security for the password or pin entry. Gaze-based authentication entails tracking

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Vicente, Francisco, Zehua Huang, Xuehan Xiong, Fernando De la Torre, Wende Zhang, and Dan Levi. "Driver Gaze Tracking and Eyes Off the Road Detection System." IEEE Transactions on Intelligent Transportation Systems 16, no. 4 (2015): 2014–27. http://dx.doi.org/10.1109/tits.2015.2396031.

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Jimenez-Pinto, J., and M. Torres-Torriti. "Face salient points and eyes tracking for robust drowsiness detection." Robotica 30, no. 5 (2011): 731–41. http://dx.doi.org/10.1017/s0263574711000749.

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SUMMARYMeasuring a driver's level of attention and drowsiness is fundamental to reducing the number of traffic accidents that often involve bus and truck drivers, who must work for long periods of time under monotonous road conditions. Determining a driver's state of alert in a noninvasive way can be achieved using computer vision techniques. However, two main difficulties must be solved in order to measure drowsiness in a robust way: first, detecting the driver's face location despite variations in pose or illumination; secondly, recognizing the driver's facial cues, such as blinks, yawns, an

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Lee, Gyung-Ju, Jin-Suh Kim, and Gye-Young Kim. "Robust pupil detection and gaze tracking under occlusion of eyes." Journal of the Korea Society of Computer and Information 21, no. 10 (2016): 11–19. http://dx.doi.org/10.9708/jksci.2016.21.10.011.

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Wu, Tunhua, Ping Wang, Shengnan Yin, and Yezhi Lin. "A New Human Eye Tracking Algorithm of Optimized TLD Based on Improved Mean-Shift." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 03 (2017): 1755007. http://dx.doi.org/10.1142/s0218001417550072.

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In this paper, an improved Mean-shift algorithm was integrated with standard tracking–learning–detection (TLD) model tracker for improving the tracking effects of standard TLD model and enhancing the anti-occlusion capability and the recognition capability of similar objectives. The target region obtained by the improved Mean-shift algorithm and the target region obtained by the TLD model tracker are integrated to achieve favorable tracking effects. Then the optimized TLD tracking system was applied to human eye tracking. In the tests, the model can be self-adopted to partial occlusion, such a

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C, Pavilaa. "Driver Drowsiness Detection System Based on Eye State Analysis." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 4791–97. http://dx.doi.org/10.22214/ijraset.2024.61050.

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Abstract: The Driver Drowsiness Detection System, utilizing eye state analysis, introduces an innovative approach with OpenCV for real-time monitoring of eye movements. This combination enables precise eye tracking and analysis, essential for assessing driver alertness. Upon detecting drowsiness, the system employs a modified Convolutional Neural Network (CNN) architecture to evaluate its severity. This neural network processes extracted features from the driver's eyes, providing a nuanced assessment of drowsiness levels. By leveraging these technologies, the system enhances safety by promptly

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A., Pooja, Arya A., Harini C., and Hifsa Naaz Syeda. "Eyes on the Road: A Comprehensive Review of Object Detection and Tracking in Autonomous Vehicles." Journal of Advance Research in Mobile Computing 7, no. 1 (2025): 38–53. https://doi.org/10.5281/zenodo.14830104.

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<em>This review explores recent advancements in object detection and tracking techniques designed for autonomous vehicles (AVs). It focuses on cutting-edge algorithms like YOLOv4, YOLOv5, and YOLOv7, which tackle key challenges such as navigating adverse weather, meeting real-time processing demands, and handling multi-object tracking. The study highlights innovations like lightweight architectures, attention mechanisms, and domain adaptation techniques that improve detection accuracy, even in complex scenarios. Diverse datasets and standardized evaluation metrics are emphasized as vital for c

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Sangeetha, S. K. B. "A survey on Deep Learning Based Eye Gaze Estimation Methods." September 2021 3, no. 3 (2021): 190–207. http://dx.doi.org/10.36548/jiip.2021.3.003.

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In recent years, deep-learning systems have made great progress, particularly in the disciplines of computer vision and pattern recognition. Deep-learning technology can be used to enable inference models to do real-time object detection and recognition. Using deep-learning-based designs, eye tracking systems could determine the position of eyes or pupils, regardless of whether visible-light or near-infrared image sensors were utilized. For growing electronic vehicle systems, such as driver monitoring systems and new touch screens, accurate and successful eye gaze estimates are critical. In de

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Jadhav, Sanjana. "Eye-Controlled Mouse for Physically Disabled Individual Using OpenCV." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04671.

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This study proposes a simple, affordable and available technology for controlling the computer cursor with only your eyes. The proposed design method was designed and developed for people with significant upper limb disabilities that don’t use a keyboard or a mouse, and has been framed as a standard input method re-defining the notion of input by an eye-re-URL sing interface. Based upon a standard web camera and real-time image processing, the eye tracking system uses the direction of an individual’s facial gaze and blinking detect and translate that into the movement needed for the cursor act

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Rashid, Maria, Wardah Mehmood, and Aliya Ashraf. "Techniques Used for Eye Gaze Interfaces and Survey." International Journal of Advances in Scientific Research 1, no. 6 (2015): 276. http://dx.doi.org/10.7439/ijasr.v1i6.2125.

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Eye movement tracking is a method that is now-a-days used for checking the usability problems in the contexts of Human Computer Interaction (HCI). Firstly we present eye tracking technology and key elements.We tend to evaluate the behavior of the use when they are using the interace of eye gaze. Used different techniques i.e. electro-oculography, infrared oculography, video oculography, image process techniques, scrolling techniques, different models, probable approaches i.e. shape based approach, appearance based methods, 2D and 3D models based approach and different software algorithms for p

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Mondal, Niladri. "Real-Time Drowsiness Detection System Using Eye-Blink Sensing and Microcontroller-Based Alert Mechanism." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48132.

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Abstract - This paper presents a real-time drowsiness detection system designed to enhance driver safety by monitoring eye activity and issuing alerts upon detecting signs of fatigue. The system is built using an Arduino Nano microcontroller integrated with an eye-blink sensor, a buzzer, a vibration motor, and a power supply circuit. The core functionality involves tracking the duration of eye closure through a sensor mounted on specialized goggles. When the eyes remain closed beyond a predefined threshold, the microcontroller activates both an audible and tactile alert to prompt driver respon

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Kiruthiga, Mrs T. "Smart Eyes for the Gentle Giants: AI-Powered Elephant Tracking." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47345.

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ABSTRACT: Elephant detection is essential for protecting these endangered animals. This paper explores how AI, especially machine learning and deep learning, can help us to spot elephants. We inspect how well different algorithms like convolutional neural networks (CNNs) and support vector machines (SVMs) work in identifying elephants in various environments. By analysing lots of images and videos, we evaluate how accurately and reliably these models perform. Our results show how the AI methods significantly improve detection rates compared to older techniques. We also discuss how important is

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Srivastava, Sanjeevani. "Driver Drowsiness Monitoring System using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 1344–50. http://dx.doi.org/10.22214/ijraset.2023.51769.

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Abstract: In today's world, sleepiness is one of the main causes of road accidents, many of which have tragic outcomes. Statistics show that the majority of traffic collisions, which frequently result in fatalities and serious injuries, are caused by sleepy driving. As a result, various studies have been done to develop software that can recognize driver tiredness and alert them before making a major error. Using methods from the automobile industry, several of the more popular ways to design their own systems. However, other factors, such as vehicle type, road design, and the capacity to oper

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Nagy, Viktor, Péter Földesi, and György Istenes. "Area of Interest Tracking Techniques for Driving Scenarios Focusing on Visual Distraction Detection." Applied Sciences 14, no. 9 (2024): 3838. http://dx.doi.org/10.3390/app14093838.

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On-road driving studies are essential for comprehending real-world driver behavior. This study investigates the use of eye-tracking (ET) technology in research on driver behavior and attention during Controlled Driving Studies (CDS). One significant challenge in these studies is accurately detecting when drivers divert their attention from crucial driving tasks. To tackle this issue, we present an improved method for analyzing raw gaze data, using a new algorithm for identifying ID tags called Binarized Area of Interest Tracking (BAIT). This technique improves the detection of incidents where

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Ram, Dr C. Sunitha, D. J. V. S. Koushik, and H. Sree Pavan. "Drowsiness Detection using EAR (Eye Aspect Ratio) by Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem19675.

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Drowsiness detection is critical in many sectors, including transportation, healthcare, and workplace safety, since it may have a substantial influence on human performance and safety. Traditional sleepiness detection approaches are frequently subjective, time intensive, and unsuitable for real-time applications. In recent years, computer vision-based techniques that use eye-related characteristics to identify tiredness have shown promise. The eye aspect ratio, a geometric measure determined from ocular landmarks that indicates the openness or closure of the eyes, is one such trait. We present

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Saleem, M. R., A. Straus, and R. Napolitano. "INTERPRETATION OF HISTORIC STRUCTURE FOR NON-INVASIVE ASSESSMENT USING EYE TRACKING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-M-1-2021 (August 28, 2021): 653–60. http://dx.doi.org/10.5194/isprs-archives-xlvi-m-1-2021-653-2021.

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Abstract. With the aims of ensuring safety and decreasing maintenance costs, previous studies in bridge inspection research have worked to elucidate damage indicators and understand their correspondence to structural deficiency. During this process, understanding how an inspector looks at a structure comprehensively as well as how they localize on damage is vital to examining diagnostic bias and how it can play a role in the preservation and maintenance process. To understand human perception and assess the humaninfrastructure interaction during the feature extraction process, eye tracking can

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Kanade, Prakash, Fortune David, and Sunay Kanade. "Convolutional Neural Networks(CNN) based Eye-Gaze Tracking System using Machine Learning Algorithm." European Journal of Electrical Engineering and Computer Science 5, no. 2 (2021): 36–40. http://dx.doi.org/10.24018/ejece.2021.5.2.314.

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To avoid the rising number of car crash deaths, which are mostly caused by drivers' inattentiveness, a paradigm shift is expected. The knowledge of a driver's look area may provide useful details about his or her point of attention. Cars with accurate and low-cost gaze classification systems can increase driver safety. When drivers shift their eyes without turning their heads to look at objects, the margin of error in gaze detection increases. For new consumer electronic applications such as driver tracking systems and novel user interfaces, accurate and effective eye gaze prediction is critic

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Byun, Hyeran, and Byoungchul Ko. "Robust Face Detection and Tracking for Real-Life Applications." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 06 (2003): 1035–55. http://dx.doi.org/10.1142/s0218001403002721.

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In this paper, we propose a new face detection and tracking algorithm for real-life telecommunication applications, such as video conferencing, cellular phone and PDA. We combine template-based face detection and tracking method with color information to track a face regardless of various lighting conditions and complex backgrounds as well as the race. Based on our experiments, we generate robust face templates from wavelet-transformed lowpass and two highpass subimages at the second level low-resolution. However, since template matching is generally sensitive to the change of illumination con

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Park, Sung Ho, Hyo Sik Yoon, and Kang Ryoung Park. "Faster R-CNN and Geometric Transformation-Based Detection of Driver’s Eyes Using Multiple Near-Infrared Camera Sensors." Sensors 19, no. 1 (2019): 197. http://dx.doi.org/10.3390/s19010197.

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Studies are being actively conducted on camera-based driver gaze tracking in a vehicle environment for vehicle interfaces and analyzing forward attention for judging driver inattention. In existing studies on the single-camera-based method, there are frequent situations in which the eye information necessary for gaze tracking cannot be observed well in the camera input image owing to the turning of the driver’s head during driving. To solve this problem, existing studies have used multiple-camera-based methods to obtain images to track the driver’s gaze. However, this method has the drawback o

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Bandewar, Prof Shailendra, Aarya A. Labhsetwar, Aditya K. Lad, et al. "Drowsiness and Yawning Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 2301–4. http://dx.doi.org/10.22214/ijraset.2023.57034.

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Abstract: Driving fatigue, drowsiness, and momentary lapses of attention, known as microsleep episodes, contribute significantly to road safety risks. This paper presents an innovative approach to address this issue by proposing a real-time drowsiness and yawning detection system that leverages a mobile camera as a non-intrusive monitoring device. The primary objective of the system is to promptly identify signs of drowsiness and yawning, alerting the driver to mitigate the potential for accidents. The system employs facial landmark tracking techniques to monitor crucial facial features such a

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Jasim, Sarah S., Alia K. Abdul Hassan, and Scott Turner. "Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Face and Eye Tracking." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 10, no. 1 (2022): 49–56. http://dx.doi.org/10.14500/aro.10928.

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It is critical today to provide safe and collision-free transport. As a result, identifying the driver’s drowsiness before their capacity to drive is jeopardized. An automated hybrid drowsiness classification method that incorporates the artificial neural network (ANN) and the gray wolf optimizer (GWO) is presented to discriminate human drowsiness and fatigue for this aim. The proposed method is evaluated in alert and sleep-deprived settings on the driver drowsiness detection of video dataset from the National Tsing Hua University Computer Vision Lab. The video was subjected to various video a

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TEJASHREE, WANI, and S. K. Kapde Prof. "VEHICLE TRACKING SYSTEM INVOLVED DROWSINESS DETECTION SYSTEM USING BUZZER & VIBRATION SENSOR." JournalNX - A Multidisciplinary Peer Reviewed Journal 2, no. 9 (2016): 56–58. https://doi.org/10.5281/zenodo.1468268.

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 Driver impairment due to drowsiness is known to be a major contributing factor in many motor vehicle crashes. More than 30% of the road accidents are caused by the fatigue of the driver. At present, there are various drowsiness detection systems available in the market. These systems are implemented using any one of the various implementation techniques such as detection of any behavioural pattern, changes in physiological conditions, or vehicular motion. Consequently, the accuracy of such systems has been found to be low..The paper is built around MCU. Here we are using eye blink sensor

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Shobaki, Walid Abdallah, and Mariofanna Milanova. "A Comparative Study of YOLO, SSD, Faster R-CNN, and More for Optimized Eye-Gaze Writing." Sci 7, no. 2 (2025): 47. https://doi.org/10.3390/sci7020047.

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Eye-gaze writing technology holds significant promise but faces several limitations. Existing eye-gaze-based systems often suffer from slow performance, particularly under challenging conditions such as low-light environments, user fatigue, or excessive head movement and blinking. These factors negatively impact the accuracy and reliability of eye-tracking technology, limiting the user’s ability to control the cursor or make selections. To address these challenges and enhance accessibility, we created a comprehensive dataset by integrating multiple publicly available datasets, including the Ey

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Collet, Christophe, Alain Finkel, and Rachid Gherbi. "CapRe: a Gaze Tracking System in Man-machine Interaction." Journal of Advanced Computational Intelligence and Intelligent Informatics 2, no. 3 (1998): 77–81. http://dx.doi.org/10.20965/jaciii.1998.p0077.

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We present a real-time camera-based system designed for gaze tracking focused on human-computer communication. We aim to equip computer systems with a tool that provides visual information on the user. This tool must satisfy interaction constraints and be nonintrusive, so we use a CCD camera placed between the keyboard and the screen. The system detects the user's presence, locates and tracks the face, nose, and eyes. Detection combines image processing and pattern recognition techniques.

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