Breaches Detection in Zebra Cross Traffic Light Using Haar Cascade Classifier

Autonomous driving needs to obtain various traffic information promptly to make decisions, and the accurate positioning of traffic light in real time is one of the key points to realize autonomous driving. Up to now, there are many interesting and practical researches related to the positioning. However, most of the scholars are doing researches of daylight traffic light location. At night, Neon lights, street lights, car lights and other factors may have a serious impact on the accurate positioning of traffic lights. How to remove the influence of these factors is a big challenge in autonomous driving. We propose a practical approach to address this challenge. The motivation of this approach is as follows: Firstly, GPS information and electronic map are used to estimate the distance and direction of the current position relative to the traffic light; secondly, binocular vision is applied to get the depth, orientation and other information of each area in the image, excluding the interference area outside the scope of the traffic light; thirdly, use some methods to positioning the traffic light, including brightness value division, geometric features analysis, circular degree detection, and then the status of the traffic light can be recognized through the HSV color space. The experimental result shows that the positioning accuracy of this method is more than 95%, the average of processing time for each image is 301ms, which can meet the requirements of accuracy and real-time.

As online shopping is becoming mainstream, driven by the social impact of Coronavirus disease-2019 (COVID-19) as well as the development of Internet services, the demand for autonomous delivery mobile robots is rapidly increasing. This trend has brought the autonomous mobile robot market to a new turning point, with expectations that numerous mobile robots will be driving on roads with traffic. To achieve these expectations, autonomous mobile robots should precisely perceive the situation on roads with traffic. In this paper, we revisit and implement a real-time traffic light recognition system with a proposed lightweight state recognition network and ratio-preserving zero padding, which is a two-stage system consisting of a traffic light detection (TLD) module and a traffic light status recognition (TLSR) module. For the TLSR module, this work proposes a lightweight state recognition network with a small number of weight parameters, because the TLD module needs more weight parameters to find the exact location of traffic lights. Then, the proposed effective and lightweight network architecture is constructed by using skip connection, multifeature maps with different sizes, and kernels of appropriately tuned sizes. Therefore, the network has a negligible impact on the overall processing time and minimal weight parameters while maintaining high performance. We also propose to utilize a ratio-preserving zero padding method for data preprocessing for the TLSR module to enhance recognition accuracy. For the TLD module, extensive evaluations with varying input sizes and backbone network types are conducted, and then appropriate values for those factors are determined, which strikes a balance between detection performance and processing time. Finally, we demonstrate that our traffic light recognition system, utilizing the TLD module’s determined parameters, the proposed network architecture for the TLSR module, and the ratio-preserving zero padding method can reliably detect the location and state of traffic lights in real-world videos recorded in Gumi and Deagu, Korea, while maintaining at least 30 frames per second for real-time operation.

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.

During the last three years, traffic accidents in Mataram City- Lombok have increased significantly. Interestingly, most of the accidents were involved high school motorcyclist. This study aims to investigate the influence of age and gender of student motorcycle riders on traffic violations and accidents, which represents a city with moderate growth. The investigation was conducted using a questionnaire survey, in which the data were collected from 394 students covering eighteen high schools. The model developed consists of behavioral, violation and accident latent variables. Behavioral constructs are comprised of five observed variables, the violation constructs consist of three observed variables, and accident constructs have two observed variables. It was found that 51.53% from 87.5% of the student’s motorcycle riders did not have the proper riding license, whom has age under 17 years old and had been involved in traffic accidents. The study was indicated a significant relationship between student riding behavior to traffic violations, and between traffic violations to accidents. Gender and age show differences in the significance (chi-square) values between riding behaviors relationship to traffic violations and accidents too.

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.

History Traffic in Indonesia can not be separated from the development of the world Automotive technology, which originated from the discovery of the engine with petroleum fuels. In the days of the Dutch East Indies government then ruled Indonesia started bringing vehicles wheel 4 and wheel 2 to Indonesia, causing traffic activity. And when the car and motorcycle multiply causing many problems - the problems of traffic, such as traffic violations and accidents. Given the above, of course of police duties particularly Traffic Police Unit increasingly severe, in the sense of Traffic Police Unit should continue to maintain and improve the technical capacity of the police in order to deal with problems like traffic violations and traffic accidents, in addition to the Police Unit Then Trails should be able to provide a breakthrough in order to increase awareness to the public about the correct procedures and safe traffic and police action to reduce the number of traffic accidents on the road.� For that to improve the professionalism and quality of work performed is necessary to standardize the work that will be used as working guidelines called the Standard Operation Procedure (SOP)Keywords: Standard Operating Procedure; Investigation; Traffice Accident.

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.

This paper proposes an eye state detection system using Haar Cascade Classifier and Circular Hough Transform. Our proposed system first detects the face and then the eyes using Haar Cascade Classifiers, which differentiate between opened and closed eyes. Circular Hough Transform (CHT) is used to detect the circular shape of the eye and make sure that the eye is detected correctly by the classifiers. The accuracy of the eye detection is 98.56% on our database which contains 2856 images for opened eye and 2384 images for closed eye. The system works on several stages and is fully automatic. The eye state detection system was tested by several people, and the accuracy of the proposed system is 96.96%.

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.

The high rate of traffic violations and accidents among adolescents is a real problem, especially when no effective solution has been found to reduce these violations. In fact, from year to year, cases of traffic violations among teenagers are increasing. The purpose of this study is to measure the meaning of adolescent life in traffic violation prevention. This type of research is a descriptive study with a quantitative approach, using a data collection tool in the form of a meaningful life questionnaire with a validity greater than 0.361 and a reliability of 0.964. The subjects of this study were vocational high school students. Subjects were taken using simple random sampling technique. This study uses data analysis techniques with descriptive analysis. The results of this study indicate that the meaning of student life consists of various aspects. Each aspect has a different percentage. The aspect of freedom of will has the lowest percentage of 10%, the aspect of decency of life has a percentage of 12%, the aspect of attitude towards death has a percentage of 23% and the aspect of purpose / meaning of life has the highest percentage of 42%. The results of this study can be used as information for counseling teachers or counselors as a basis for efforts to provide assistance to students to increase the meaning of life in order to prevent negative behavior, one of which is traffic violations that often occur in adolescents.

Based on the accident data’s, which recorded that 2016 until 2019, there were everage 225 traffic accidents involving students and college student riders. From these accident data’s, was found that the male rider dominated traffic accidents than female rider. This study aims to determine how a simple model can be done based on difference a demographic aspect such as differences of gender influences, differences of economic and social aspect on the driving behavior of student and college students on traffic accidents in Mataram City. The Structural Equation Modeling (SEM) method, with the help of the AMOS program was used in this study. The number of respondents in this study were 792 people, consisting of 391 students and 391 college students. The model in this study consisted of 3 variables, namely behavior, violations and accidents. Driving behavior has a significant effect on traffic violations with a probability value (P) = 0.000 (<0.001), and a testing value of C.R> 1.64. The size of the estimated value in the student group shows that the effect of behavior on violations in male student rider is 30.08% stronger than that of female rider. And the effect of behavior on violations among female students is 0.8% stronger than college students. Traffic violations have a significant effect on traffic accidents with a probability value of 0.000 (<0.01), and a test value of C.R> 1.96. Traffic violations on male students have an effect on accidents by 0.992, 50.1% stronger than female students. The effect of traffic violations on student rider on accidents is 2.44% stronger than female student rider. Gender differences have a significant effect on driving behavior based on the probability value (P) = 0.000 (<0.001). On the otherhand, the results of the analysis show that the driving behavior model of the community on the Mataram City based on the review of the economic and social aspects shows a model with a variety of patterns where the GRDP review follows the Polynomial model, based on the number of cityzen population, the tendency of the Exponential model and the review of the gender probability aspect follows the Logarithmic model pattern

The traffic accident and violation rates of Washington's 39,242 medically restricted drivers were compared with the accident and violation rates of all 1.6 million licensed Washington drivers. Statistically higher accident rates were reported for persons whose licenses are restricted as a result of diabetes, epilepsy, fainting, and other conditions. Persons with a license restriction for heart disease had slightly, though not statistically, higher accident rates, and persons with vision deterioration had slightly, though not statistically, lower accident rates. Statistically higher violation rates were recorded for persons with diabetes, epilepsy, and other conditions. The violation rate for the group with heart disease was statistically lower. The group with physical handicaps such as paralysis, loss of limb, stabilized vision defects, etc, had a statistically higher accident rate and a slightly, though not statistically, higher violation rate.