Real-time eye state detection system using haar cascade classifier and circular hough transform

Emergency Medical Services (EMS) require rapid response and efficient routing to ensure timely patient care. However, urban traffic congestion and static routing methods often delay ambulance arrivals. To addressthis, this paper proposes an intelligent ambulance routing and traffic-signal preemption framework, termed SLnO-CC, integrating Sea Lion Optimization (SLnO) for optimal route planning and a Haar Cascade Classifier (CC) for real-time emergency vehicle detection and signal control. The proposed model wasevaluated across eight real-world traffic scenarios within a 15 km urban area, benchmarking against A*, Advanced A* with Dispersion Index, Ant Colony Optimization (ACO), and standalone SLnO. Experimental results demonstrate that SLnO-CC achieved the lowest average response time (9.06 min) and travel time(5.36 min), outperforming A* (9.70 min, 12.20 min) and ACO (9.44 min, 11.47 min) by 6.6% and 13.2%, respectively. In terms of total routing efficiency, SLnO-CC reduced the overall distance and time by 17.8% and 19.6%, respectively, compared with existing baselines. The Haar Cascade–based preemption module achieved 96.8% detection accuracy under varying illumination and occlusion. Overall, the SLnO-CC framework enhances routing adaptability, congestion awareness, and emergency responsiveness—ensuring total response time remains within 10 minutes over a 15 km operational range with high detection reliability.

In this paper, a new iris localization method for mobile devices is presented. Our system uses both intensity and saturation threshold on the captured eye images to determine iris boundary and sclera area, respectively. Estimated iris boundary pixels which have been placed outside the sclera will be removed. The remaining pixels are mainly the boundary of iris inside the sclera. Then, circular Hough transform is applied to such iris boundary pixels in order to localize the iris. Experiments were done on 60 iris images taken by a HTC mobile device from 10 different persons with both left and right eyes images available per person. Also, we evaluate the proposed algorithm on MICHE datasets include iphone5, Samsung Galaxy S4 and Samsung Galaxy Tab2. Experimental evaluation shows that the proposed system can successfully localize iris on tested images.

This paper proposes an accurate iris localization algorithm for the iris images acquired under near infrared (NIR) illuminations and having noise due to eyelids, eyelashes, lighting reflections, non-uniform illumination, eyeglasses and eyebrow hair etc. The two main contributions in the paper are an edge map generation technique for pupil boundary detection and an adaptive circular Hough transform (CHT) algorithm for limbic boundary detection, which not only make the iris localization more accurate but faster also. The edge map for pupil boundary detection is generated on intersection (logical AND) of two binary edge maps obtained using thresholding, morphological operations and Sobel edge detection, which results in minimal false edges caused by the noise. The adaptive CHT algorithm for limbic boundary detection searches for a set of two arcs in an image instead of a full circle that counters iris-occlusions by the eyelids and eyelashes. The proposed CHT and adaptive CHT implementations for pupil and limbic boundary detection respectively use a two-dimensional accumulator array that reduces memory requirements. The proposed algorithm gives the accuracies of 99.7% and 99.38% for the challenging CASIA-Iris-Thousand (version 4.0) and CASIA-Iris-Lamp (version 3.0) databases respectively. The average time cost per image is 905 msec. The proposed algorithm is compared with the previous work and shows better results.

Based on a survey released by the TomTom Traffic Index in 2018, Indonesia was ranked seventh in the category of the most congested country in the world. One of the factors affecting traffic congestion in Indonesia is an inflexible and conventional traffic management system. In this regard, it is necessary to have a better traffic management system such as a Smart Traffic Light. One way to implement a smart traffic light system is to make a vehicle detection and counting system on the traffic CCTV video automatically. The methods used in this research are Haar Cascade Classifiers and Convolutional Neural Network. Haar Cascade Classifiers have fast computation processes and CNN is applied to validate the detection results of the Haar Cascade method for better accuracy. The average level of accuracy achieved by the system on quiet test data is 82%, normal test data is 69%, and busy test data is 60%. Meanwhile, the average computation time needed by the system for the quiet test data is 0.63 seconds, the normal test data is 0.52 seconds, and the busy test data is 1.05 seconds.

This paper presents a method for particle location based on Circular Hough Transform and Quadrant Radius Histogram in Particle Image Velocimetry. Different from the existing approaches, 2D Gaussian function is not used to describe the particle intensity profile because of defocusing. In proposed method, Circular Hough Transform was employed to detect the particle and calculate the initial particle center coordinate, and then the Quadrant Radius Histogram was applied to modify the initial particle center coordinate more accurately. Experiments on real particle images have demonstrated the feasibility of the proposed method for focused and defocused particle location, and the mean MAE of the distance between the estimated particle center and the real particle center for all experimental particle images is about 0.624 pixels.

In this chapter, a human detection system based on unsupervised learning method K-means clustering followed by deep learning approach You Only Look Once (YOLO) on thermal imagery has been proposed. Generally, images in the visible spectrum are used to conduct such human detection, which are not suitable for nighttime due to low visibility, hence for evaluation of our system. Hence, long wave infrared (LWIR) images have been used to implement the proposed work in this chapter. The system follows a two-step approach of generating anchor boxes using K-means clustering and then using those anchor boxes in 252 layered single shot detector (YOLO) to predict proper boundary boxes. The dataset of such images is provided by FLIR company. The dataset contains 6822 images for training purposes and 757 images for the validation. This proposed system can be used for real-time object detection as YOLO can achieve much higher rate of processing when compared to traditional method like HAAR cascade classifier in long wave infrared imagery (LWIR).

The paper proposes a self-driving car model also called autonomous, robotic or driver-less car is one that operates and navigates using its intelligence. The basic idea behind the paper is to develop a 1/10 scale RC car to portray an automated car. The model consists of the following software and hardware components such as CNN (Convolutional neural network), Monocular vision algorithm, Haar cascade classifier, Raspberry Pi Board model B+, Pi camera, Arduino, and an Ultrasonic sensor. The Pi camera and ultrasonic sensor are attached to the raspberry pi board to collect input images along with sensor data to stream these data to the server which in our case is the laptop. The (CNN) convolutional neural network running on the server will be used to enable lane detection to provide steering predictions that are left, right, forward based on the input image. The haar cascade classifier will be used to detect signals and stop sign and monocular vision algorithms to calculate distance from them. The ultrasonic sensor will be used for front collision avoidance by stopping the car at a certain distance before the obstacle ahead. The navigation commands such as right, left, forward, stop will be sent to the car through Arduino which is connected to the RC car's remote, this will make the car drive autonomously based on neural network predictions and some hard coded rules. Thus this model will enable autonomous driving by self-navigation via lane detection, stopping at detection of stop signs, red lights and moving on green signal and front collision avoidance in a cost-effective manner. Hardcoded rules, obstacle avoidance mechanisms, machine learning models, and smart object discrimination will help the system follow traffic rules and navigate the car using artificial intelligence.

The purpose of the paper is to recognize an abducted child from the photos of children available, with the help of face recognition and face matching technique and to adopt an abandoned child from the data available, with the help of string comparing technique. The work in this paper is extended by providing the concept of an adoption system for the abandoned child. Haar cascade classifier, an OpenCV classifier can be used for face recognition, the mean method can be used for face matching, and DB can be used for string comparisons. The dataset consists of 500 images for child recognition and child matching, and 50 images for child adoption. The accuracy obtained from the face recognition algorithm is 80.7%.KeywordsHaar cascade classifierJSON modelOpenCV and DB

The Face -Based Attendance System is an innovative solution designed to automate and streamline the process of recording attendance in academic institutions and workplaces. Traditional methods, such as manual sign-ins and RFID-based systems, are often time-consuming and susceptible to proxy attendance. This project leverages advanced computer vision and machine learning techniques, particularly using Open CV and the Haar Cascade Classifier, to detect and recognize faces in real time. The system captures live video input through a webcam, processes the facial features, and matches them with stored profiles to mark attendance automatically. This approach not only improves accuracy and efficiency but also enhances security and reduces administrative overhead. The system is implemented using Python and integrates a graphical user interface (GUI) for ease of use. Overall, this project demonstrates the potential of facial recognition technology in creating smart, secure, and contact-less attendance systems. Keywords: Face recognition, attendance system, computer vision, machine learning, OpenCV, Haar Cascade Classifier, real-time detection, Python, automation, contactless attendance.

Accidents related with vulnerable pedestrians around crosswalks are continued so that proactive safety support system is required. Pedestrian detection from frames captured by a camera is a significant and yet challenging task. An autonomous road crossing surveillance system would be ideal for tracking pedestrians who want to cross the road and assist them. A practical solution for aiding pedestrians regularly, a road crossing surveillance with real-time Pedestrian Detection. Since the background subtraction from videos and images is still a persistent problem and difficult to accomplish. A Haar Cascade Classifier with the full-body detection algorithm is used to detect people in real-time captured by a camera. Keywords: Pedestrian crossing, surveillance, road crossing surveillance, surveillance system, autonomous, Haar Cascade Classifier.

Traditional attendance systems often encounter challenges in efficiently and accurately recording attendance. This research aims to introduce an innovative solution through the development of an intelligent anti-spoofing attendance system based on facial recognition using the Haar Cascade Classifier method. Designed to overcome the inefficiencies in attendance recording, this system ensures the accuracy of educational staff attendance records. Its development method relies on the Haar Cascade Classifier, employing image processing to detect learned object features, particularly focusing on facial recognition. Research findings indicate that the implementation of this system achieves an average accuracy rate of 98.90% in attendance recording. The facial recognition technology ensures reliable attendance recording with confidence levels exceeding 80%, signifying precise facial identification that addresses various challenges and ensures attendance data integrity. Not only does the system identify educational staff with high accuracy, but it also provides prompt responses for efficient attendance logging and verification. Beyond its technical benefits, this study significantly contributes to the development of smarter and more efficient attendance technology. The system plays a crucial role in enhancing the discipline of educational staff at STMIK IKMI Cirebon and streamlining attendance management and evaluation across educational institutions.

Traffic violations are common in the area zebra crossing at the location of traffic lights, violations are generally caused by the negligence of motorists who do not comply with existing regulations. As a result, there were many traffic accidents that could have been avoided. Starting from this problem, a simulation model tool is needed that is able to provide direct appeals to drivers when a violation occurs. Based on this, the purpose of this research is to create a simulation model to prevent accidents caused by motorists who are negligent of the rules by identifying vehicles that stop crossing the line boundaries. zebracross or drivers waiting for an out of place traffic light using imagery HaarCascade Classifiers which is programmed using Python and OpenCV to help process digital images. The results of this study that the Haar Cascade Classifier algorithm can characterize motorbikes well, with the best average accuracy value of 91.5% at a resolution of 720p. Based on the results obtained shows that the algorithm haar cascade classifier can detect violations that exist at traffic lights. Hence this indicates the rate of traffic violations can reduce by utilizing the algorithm haar cascade classifier.

Many diseases such as anemia and leukemia are detected by counting Red Blood Cells (RBCs or erythrocytes). Generally, there are manual and automatic method for RBCs counting. In the manual method, RBCs counting is performed by a hematologist with the help of special medical equipment. The manual method is tedious, time-consuming and dependent on equipment and hematologist's expert and accuracy. The automatic method is fast, high accuracy and independent of equipment and hematologist. It is performed with the assistance of a microscopic image of the person's blood and a computer system. In this paper, a new method is presented for counting incomplete or cropped RBCs by Circular Hough Transform (CHT) and another method called EndPoints method which will be described in the following. Two parallel tasks are performed on the input image. In the first work, the input image is segmented using thresholding and erosion on the green channel. In the second work, the input image is converted to a grayscale image and edged by the Canny method. Then, the segmented image is subtracted from the edged image and remove White Blood Cells (WBCs or leucocytes) from it. After margin is added, by defining EndPoints set estimated continuation of RBCs will be added to the image. Finally, CHT is applied to the image and it will calculate the number of RBCs. Also, new counting error and counting accuracy metrics are defined for counting problem. The method is evaluated on the fifteen images of the ALL-IDB1 database and achieved 97.14 overall accuracy.

In this study, specifically for the detection of ripe/unripe tomatoes with/without defects in the crop field, two distinct methods are described and compared from captured images by a camera mounted on a mobile robot. One is a machine learning approach, known as ‘Cascaded Object Detector’ (COD) and the other is a composition of traditional customised methods, individually known as ‘Colour Transformation’: ‘Colour Segmentation’ and ‘Circular Hough Transformation’. The (Viola‐Jones) COD generates ‘histogram of oriented gradient’ (HOG) features to detect tomatoes. For ripeness checking, the RGB mean is calculated with a set of rules. However, for traditional methods, colour thresholding is applied to detect tomatoes either from natural or solid background and RGB colour is adjusted to identify ripened tomatoes. This algorithm is shown to be optimally feasible for any micro‐controller based miniature electronic devices in terms of its run time complexity of O(n3) for a traditional method in best and average cases. Comparisons show that the accuracy of the machine learning method is 95%, better than that of the Colour Segmentation Method using MATLAB.

New crescent moon detection using Circular Hough Transform (CHT)