31 skills found · Page 1 of 2
dolongbien / HumanBehaviorBKUAbnormal Human Behaviors Detection/ Road Accident Detection From Surveillance Videos/ Real-World Anomaly Detection in Surveillance Videos/ C3D Feature Extraction
khaledsabry97 / Argus[IEEE Research paper + Project] Real Time Road Accidents Detection System based on crash estimation; a computer vision techniques that detects road accidents and reports them in real-time as well as allowing the monitoring of accidents using a client server architecture and an interactive GUI.
BilmiaBinson / Road Accident Detection Alert SystemNo description available
rj97 / Accident Detection On Indian RoadsAutomatic incident detection on Indian Roads using Artificial Intelligence. Model is trained using Tensorflow object detection API. Use of model after training in Python script as well as in Android application is also demonstrated in this project.
agdhruv / Accident MonitoringAutomatic detection and reporting of road accidents using a machine learning and vision algorithm. Built as a Smart City project at HackIIITD.
Akshaydatascience / RoadAccident Detection Alert SystemNo description available
VishalRMahajan / NirikshanNirikshan: An AI-powered real-time road accident detection system using YOLOv11 and CCTV footage, built with FastAPI & Next.js for instant alerts and emergency response.
LeadingIndiaAI / LANE DETECTION USING DEEP LEARNINGAutonomous self-driving is in the trend for implementing it in our real life to remove all the hassles and accidents. Modern-day transport has come a long way but still far away from perfection and all-around safety. Lane Detection is a concept of demarcating lanes on the roads while the vehicle is moving. It has the capability of changing the vehicular movements on road, making them more organized and safe. This leap could provide for driver carelessness and avoid a lot of mishaps on the roads. Ride-hailing services like Uber and Ola can use them to monitor drivers and rate them based on driving skills. We have designed and trained a deep Convolutional Network model from scratch for lane detection since a CNN based model is known to work best for image datasets. We have used BDD100k dataset for training and testing for our model. We have used various metrics values for hyper-parameters tuning and took the ones which gave the best result. The training is done on Supercomputer NVIDIA-DGX V100. Idea By: Aditya Sharma, Microsoft
20BAI4038 / Road Accident DetectionNo description available
Soham2212004 / Road Accident Detection Alert SystemNo description available
ZTR02 / STTADSTTAD is a dataset we propose for road traffic accident detection from surveillance views, featuring fine-grained classification and spatiotemporal annotations. This repository provides examples from the training and testing sets to showcase the data structure. The full dataset and related algorithms will be gradually updated.
DivanshiJain2005 / Driver Drowsiness DetectionA real-time driver drowsiness detection system using Haar Cascade for face detection, LSTM for sequential analysis, and CNN for feature extraction, achieving 95.1% accuracy. The system monitors eye closure patterns and triggers alerts to prevent accidents and enhance road safety.
draxler1 / MP RAD Dataset ITS MP-RAD Dataset for the paper, "Detection of Road Accidents using Synthetically Generated Multi-Perspective Accident Videos", published in IEEE Transactions on Intelligent Transportation Systems , 2022.
Darshan-Gaidhane / IMPLEMENTING A SYSTEM TO DETECT DRIVER DROWSINESS USING MACHINE LEARNINGEvery year many people lose their lives due to fatal road accidents around the world and Drowsiness and Fatigue of drivers are amongst the significant causes of road accidents. Alcohol, Overwork, Stress, and even Medical conditions can cause drivers to fall sleep. It is very important to detect the drowsiness of the driver to save life and property. So to reduce the accidents and save the life of a driver we propose to develop a system called as Driver Drowsiness Detection (D3 ) system. This system can automatically detect driver drowsiness in a real-time video stream and then play an alarm if the driver appears to be drowsy. Haar Cascade classifier, facial landmarks and computing Eye Aspect Ratio (EAR) to ensure proper detection of drowsiness in order to avoid accidents. For implementing this system we used libraries like Opencv and dlib.
Devang-25 / Accident Monitoring System MAJOR PROJECTAutomatic detection and Reporting of Road Accidents using a Machine Learning and Vision Algorithm. Built as a MAJOR PROJECT for MAIT CSE.
WaqasJafar / DriveSafe AppDriver drowsiness is the most critical cause of road accidents so detection of drowsiness play a vital role in preventing road accidents. We are developing an android app that will alert drivers before an accident occurs. This will reduce the number of road accidents on a road. Drowsiness is a natural phenomenon that happens in human body due to different factors. Machine learning was applied to predict drowsiness and improve drowsiness prediction using facial recognition technology and eye-blink recognition technology. In this app, front camera will take a picture of drowsy driver then this picture will be taken as input. In processing the detected image, we are using OpenCV Library. OpenCV Library uses Haar Cascade Classifier for detection images such as eyes and face. Eyes and face will be the target in this system. This application will be implemented on Android Operating System. Drowsiness detection system will send alert to the driver when the driver feels asleep while driving a car, this can avoid accidents. Driver which is the user in this application, if they close their eyes within one second, the sensor which is the front camera in the smartphone will catch and process this event and then trigger the system to give voice alert to the user. Moreover, if the driver is willing to turn on back camera then it will detect the lane detection violation and will calculate the distance from the vehicle ahead of it. If the distance is too close, then it will generate an alarm. It will also generate an alarm if there is a violation of the lane on the road.
menu23 / Driver Fatigue DetectionA fatigue detection and alert system that can be easily installed in vehicles to prevent road accidents. The system runs on a Raspberry Pi module and works by analyzing the eye closure duration and yawn frequency and alerting the driver by activating LEDs, buzzers and sending warning message to his emergency contacts.
ngowtham1296 / Traffic Sign Detection ADASObject identification has many applications in various fields like autonomous vehicles. In all over the world, important information about the road condition and its limitations are introduced to drivers as visual signals, such as traffic signs. Traffic signs are an important part of road infrastructure to provide information about the condition of road, warnings, prohibition, restriction, and other helpful information to the driver for navigation. They provide important information which can be interpreted by drivers. During inferior traffic or bad weather conditions, driver may not notice the signs directly or indirectly, which may lead to accidents or serious injuries. During such circumstances, if there is an automatic detection system for traffic signs, it can warn driver of such signs on the road and help him follow such signs and thus making driving safe. Advanced driver assistance system (ADAS) is one of the fastest growing fields in autonomous vehicle. ADAS technology is completely based upon vision system, active sensor technology and car data network. A vision-based road sign detection system is thus necessary to catch the driver’s attention to avoid any accidents. However, there are many factors which make the road sign detection difficult such as lighting conditions poor or bright, deformation of signs, angle at which they are placed. Thus, our aim in this project is to write an algorithm for vision-based traffic sign Identification.
lrmicmc / DatasetRoad traffic crashes are the leading cause of death among young people between 10 and 24 years old. In recent years, both academia and industry have been devoted towards the development of Driver Assistance Systems (DAS) and Autonomous Vehicles (AV) to decrease the number of road accidents. Detection of the road surface is a key capability for both path planning and object detection on Autonomous Vehicles. Current road datasets and benchmarks only depict European and North American scenarios, while emerging countries have higher projected consumer acceptance of AV and DAS technologies. This paper presents a selected Brazilian urban scenario dataset and road detection benchmark consisting of annotated RADAR, LIDAR and camera data. It also proposes a novel evaluation metric based on the intersection of polygons. The main goal of this manuscript is to provide challenging scenarios for road detection algorithm evaluation and the resulting dataset is publicly available at www.lrm.icmc.usp.br/dataset.
hemanth1403 / Driver Assistance SystemThis project improves road safety with an Driver Assistance System (DAS) that includes drowsiness alerting, lane detection and keeping, object detection, and collision warning. Utilizing YOLO and OpenCV, it monitors drivers and surroundings to enhance safety and prevent accidents.
