Download or read book Computer Vision based Traffic Sign Detection and Extraction a Hybrid Approach Using GIS and Machine Learning written by Zihao Wu and published by . This book was released on 2019 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt: Author's abstract: Traffic sign detection and positioning have drawn considerable attention because of the recent development of autonomous driving and intelligent transportation systems. In order to detect and pinpoint traffic signs accurately, this research proposes two methods. In the first method, geo-tagged Google Street View images and road networks were utilized to locate traffic signs. In the second method, both traffic signs categories and locations were identified and extracted from the location-based GoPro video. TensorFlow is the machine learning framework used to implement these two methods. To that end, 363 stop signs were detected and mapped accurately using the first method (Google Street View image-based approach). Then 32 traffic signs were recognized and pinpointed using the second method (GoPro video-based approach) for better location accuracy, within 10 meters. The average distance from the observation points to the 32 ground truth references was 7.78 meters. The advantages of these methods were discussed. GoPro video-based approach has higher location accuracy, while Google Street View image-based approach is more accessible in most major cities around the world. The proposed traffic sign detection workflow can thus extract and locate traffic signs in other cities. For further consideration and development of this research, IMU (Inertial Measurement Unit) and SLAM (Simultaneous Localization and Mapping) methods could be integrated to incorporate more data and improve location prediction accuracy.

Download or read book Traffic Sign Recognition Systems written by Sergio Escalera and published by Springer Science & Business Media. This book was released on 2011-09-22 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work presents a full generic approach to the detection and recognition of traffic signs. The approach is based on the latest computer vision methods for object detection, and on powerful methods for multiclass classification. The challenge was to robustly detect a set of different sign classes in real time, and to classify each detected sign into a large, extensible set of classes. To address this challenge, several state-of-the-art methods were developed that can be used for different recognition problems. Following an introduction to the problems of traffic sign detection and categorization, the text focuses on the problem of detection, and presents recent developments in this field. The text then surveys a specific methodology for the problem of traffic sign categorization – Error-Correcting Output Codes – and presents several algorithms, performing experimental validation on a mobile mapping application. The work ends with a discussion on future research and continuing challenges.

Download or read book Video Based Machine Learning for Traffic Intersections written by Tania Banerjee and published by CRC Press. This book was released on 2023-10-17 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Video Based Machine Learning for Traffic Intersections describes the development of computer vision and machine learning-based applications for Intelligent Transportation Systems (ITS) and the challenges encountered during their deployment. This book presents several novel approaches, including a two-stream convolutional network architecture for vehicle detection, tracking, and near-miss detection; an unsupervised approach to detect near-misses in fisheye intersection videos using a deep learning model combined with a camera calibration and spline-based mapping method; and algorithms that utilize video analysis and signal timing data to accurately detect and categorize events based on the phase and type of conflict in pedestrian-vehicle and vehicle-vehicle interactions. The book makes use of a real-time trajectory prediction approach, combined with aligned Google Maps information, to estimate vehicle travel time across multiple intersections. Novel visualization software, designed by the authors to serve traffic practitioners, is used to analyze the efficiency and safety of intersections. The software offers two modes: a streaming mode and a historical mode, both of which are useful to traffic engineers who need to quickly analyze trajectories to better understand traffic behavior at an intersection. Overall, this book presents a comprehensive overview of the application of computer vision and machine learning to solve transportation-related problems. Video Based Machine Learning for Traffic Intersections demonstrates how these techniques can be used to improve safety, efficiency, and traffic flow, as well as identify potential conflicts and issues before they occur. The range of novel approaches and techniques presented offers a glimpse of the exciting possibilities that lie ahead for ITS research and development. Key Features: Describes the development and challenges associated with Intelligent Transportation Systems (ITS) Provides novel visualization software designed to serve traffic practitioners in analyzing the efficiency and safety of an intersection Has the potential to proactively identify potential conflict situations and develop an early warning system for real-time vehicle-vehicle and pedestrian-vehicle conflicts

Download or read book Video Based Machine Learning for Traffic Intersections written by Tania Banerjee (Computer scientist) and published by . This book was released on 2023-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Video Based Machine Learning for Traffic Intersections describes the development of computer vision and machine learning-based applications for Intelligent Transportation Systems (ITS) and the challenges encountered during their deployment. This book presents several novel approaches, including a two-stream convolutional network architecture for vehicle detection, tracking, and near-miss detection; an unsupervised approach to detect near-misses in fisheye intersection videos using a deep learning model combined with a camera calibration and spline-based mapping method; and algorithms that utilize video analysis and signal timing data to accurately detect and categorize events based on the phase and type of conflict in pedestrian-vehicle and vehicle-vehicle interactions. The book makes use of a real-time trajectory prediction approach, combined with aligned Google Maps information, to estimate vehicle travel time across multiple intersections. Novel visualization software, designed by the authors to serve traffic practitioners, is used to analyze the efficiency and safety of intersections. The software offers two modes: a streaming mode and a historical mode, both of which are useful to traffic engineers who need to quickly analyze trajectories to better understand traffic behavior at an intersection. Overall, this book presents a comprehensive overview of the application of computer vision and machine learning to solve transportation-related problems. Video Based Machine Learning for Traffic Intersections demonstrates how these techniques can be used to improve safety, efficiency, and traffic flow, as well as identify potential conflicts and issues before they occur. The range of novel approaches and techniques presented offers a glimpse of the exciting possibilities that lie ahead for ITS research and development"--

Download or read book Road Traffic Modeling and Management written by Fouzi Harrou and published by Elsevier. This book was released on 2021-10-05 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Road Traffic Modeling and Management: Using Statistical Monitoring and Deep Learning provides a framework for understanding and enhancing road traffic monitoring and management. The book examines commonly used traffic analysis methodologies as well the emerging methods that use deep learning methods. Other sections discuss how to understand statistical models and machine learning algorithms and how to apply them to traffic modeling, estimation, forecasting and traffic congestion monitoring. Providing both a theoretical framework along with practical technical solutions, this book is ideal for researchers and practitioners who want to improve the performance of intelligent transportation systems. Provides integrated, up-to-date and complete coverage of the key components for intelligent transportation systems: traffic modeling, forecasting, estimation and monitoring Uses methods based on video and time series data for traffic modeling and forecasting Includes case studies, key processes guidance and comparisons of different methodologies

Download or read book Video Based Machine Learning for Traffic Intersections written by Tania Banerjee and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Video Based Machine Learning for Traffic Intersections describes the development of computer vision and machine learning-based applications for Intelligent Transportation Systems (ITS) and the challenges encountered during their deployment. This book presents several novel approaches, including a two-stream convolutional network architecture for vehicle detection, tracking, and near-miss detection; an unsupervised approach to detect near-misses in fisheye intersection videos using a deep learning model combined with a camera calibration and spline-based mapping method; and algorithms that utilize video analysis and signal timing data to accurately detect and categorize events based on the phase and type of conflict in pedestrian-vehicle and vehicle-vehicle interactions. The book makes use of a real-time trajectory prediction approach, combined with aligned Google Maps information, to estimate vehicle travel time across multiple intersections. Novel visualization software, designed by the authors to serve traffic practitioners, is used to analyze the efficiency and safety of intersections. The software offers two modes: a streaming mode and a historical mode, both of which are useful to traffic engineers who need to quickly analyze trajectories to better understand traffic behavior at an intersection. Overall, this book presents a comprehensive overview of the application of computer vision and machine learning to solve transportation-related problems. Video Based Machine Learning for Traffic Intersections demonstrates how these techniques can be used to improve safety, efficiency, and traffic flow, as well as identify potential conflicts and issues before they occur. The range of novel approaches and techniques presented offers a glimpse of the exciting possibilities that lie ahead for ITS research and development. Key Features: Describes the development and challenges associated with Intelligent Transportation Systems (ITS) Provides novel visualization software designed to serve traffic practitioners in analyzing the efficiency and safety of an intersection Has the potential to proactively identify potential conflict situations and develop an early warning system for real-time vehicle-vehicle and pedestrian-vehicle conflicts

Download or read book Traffic Sign Recognition written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2024-05-04 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is Traffic Sign Recognition Traffic-sign recognition (TSR) is a technology by which a vehicle is able to recognize the traffic signs put on the road e.g. "speed limit" or "children" or "turn ahead". This is part of the features collectively called ADAS. The technology is being developed by a variety of automotive suppliers. It uses image processing techniques to detect the traffic signs. The detection methods can be generally divided into color based, shape based and learning based methods. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Traffic-sign recognition Chapter 2: Traffic sign Chapter 3: Intelligent transportation system Chapter 4: Electronic stability control Chapter 5: Advanced driver-assistance system Chapter 6: Lane departure warning system Chapter 7: Adaptive cruise control Chapter 8: Intelligent speed assistance Chapter 9: Driver monitoring system Chapter 10: Collision avoidance system (II) Answering the public top questions about traffic sign recognition. (III) Real world examples for the usage of traffic sign recognition in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Traffic Sign Recognition.

Download or read book Traffic Data Extraction from Drones Using Deep Learning based Computer Vision Methods written by Bahaa alddin Mansour and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traffic monitoring systems provide valuable traffic information desired by transport engineers to enhance transportation planning and traffic management. Recently, Unmanned Aerial Vehicles (UAV), also known as drones, have opened many opportunities for different traffic monitoring applications, ranging from traffic surveillance applications to surrogate safety measures. UAVs hold definite advantages over the traditional traffic sensors as they are well known for easy manoeuvring, low cost, wider field of view and no disturbance on traffic which translates into a safer and quicker data collection strategy. In parallel, with the outbreak of deep learning technology, the use of computer vision to automatically extract traffic flow data from drone videos has become a promising option for UAV-based applications. Several systems have been proposed in the literature that exploits different computer vision approaches for traffic data extraction. These methods can be categorised as flow-based, appearance-based and object-based. Most of these methods focused on extracting traffic data from fixed surveillance cameras (CCTV) located on highways. However, all of the reviewed methods have their own limitations and might not suit complex data collection situations such as signalised intersections or roundabouts. This thesis proposes a novel method to extract lane-by-lane traffic flow data automatically from drone video footage. Deep learning-based methods namely YOLO-v3 and Sparse Lucas-Kanade Optical Flow techniques are employed to detect, categorise (light vehicle/heavy vehicle) and track vehicles while Open Source Computer Vision (OpenCV) is used to write codes to extract vehicle count, headway and queue length data from the video footage. The proposed methods are verified for its computational efficiency and accuracy using drone video footages taken from a signalised intersection in Auckland, New Zealand. The results are then compared with those reported in the literature. The proposed methods demonstrate a prospect to improve computational efficiency as well as the accuracy of traffic data extraction from drone video footage.

Download or read book Computer Vision and Machine Learning in Sustainable Mobility The Case of Road Surface Defects written by Sromona Chatterjee and published by Cuvillier Verlag. This book was released on 2020-08-18 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: Road maintenance has traditionally been a time consuming, expensive, and manual process. Timely maintenance of roads helps in lowering rehabilitation costs, accidents, environmental pollution, while facilitating increased connectivity, trade, and growth. Easily acquirable front-view scene images are seen to be used lately for infrastructure management and road maintenance as they provide quicker, low-cost, and flexible solutions. Such scene images can easily be acquired using standard commodity cameras. In this dissertation, machine learning based approaches have been developed to analyze front-view scene images for detecting cracks automatically on road surfaces across different locations and under various conditions. This work thus contributes toward automated approaches to detect different kinds of cracks on road surfaces, thereby proposing a low-cost solution to road maintenance practices. As a result, different components are developed in this work which are sketched together to form a Decision Support System for the task of crack detection. In this study primarily three algorithmic approaches have been developed. Firstly, an unsupervised graph-based hierarchical clustering technique for road area segmentation has been developed, thus helping in detecting the road area in scene images. Secondly, a classifier and superpixel based supervised learning approach consisting of systematically identifying relevant features for detecting superpixels containing cracks has been developed. Thirdly, an unsupervised learning approach consisting of Gamma Mixture Fuzzy Model based clustering technique and keypoint matching mechanisms have been designed in this work for detecting which road pixels are crack pixels in images. Finally, this study integrates the findings and approaches to propose a Decision Support System for crack detection on road surfaces of easily acquirable front-view scene images. Evaluations performed on an experimentally collected diverse front-view scene image dataset show promising results for crack detection using the developed approaches in this work.

Download or read book Detect Traffic Signs from Large Street View Images with Deep Learning written by Zhifei Deng and published by . This book was released on 2020 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous driving is about to shaping the future of our life. Self-driving vehicles produced by Waymo or many other companies have demonstrated excellent driving capabilities on the road. However, accidents still happen. Correctly recognising the traffic signs, such as stop signs, is critical for a self-driving vehicle. Failing to recognise the traffic signs could lead to fatal accidents. Meanwhile, computer vision technology has made huge progress since the advent of deep learning, for example, image classification, object detection, and instance segmentation. Efforts have been made in developing faster and more accurate object detection methods. Faster RCNN stands out as one of the most popular framework for object detection. Although frameworks like Faster R-CNN achieved state-of-the-art results in generic object detection, few endeavours have been made for traffic sign detection. Detecting traffic signs from street view images is much more challenging than detection of generic objects from natural images. Street view images have high resolution, while traffic sign tends to be small in those images. Complex background in street view images also adds more difficulty in detecting traffic signs. In this thesis, we proposed a novel two-stage object detection method for solving the challenging problem of detecting traffic signs from large street view images. In the first stage, we detect some less accurate regions which might contain traffic signs. Then we zoom in those candidate regions, and find the exact location of traffic signs in the second stage.The proposed method achieves AP (average precision) of 0.85 on a large street view dataset from an industry partner, which outperforms Faster R-CNN greatly, whose AP is around 0.13. The result reflects the potential of using the two-stage approach to detect small objects from high resolution images.

Download or read book Nonlinear Model Predictive Control written by Frank Allgöwer and published by Birkhäuser. This book was released on 2012-12-06 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the past decade model predictive control (MPC), also referred to as receding horizon control or moving horizon control, has become the preferred control strategy for quite a number of industrial processes. There have been many significant advances in this area over the past years, one of the most important ones being its extension to nonlinear systems. This book gives an up-to-date assessment of the current state of the art in the new field of nonlinear model predictive control (NMPC). The main topic areas that appear to be of central importance for NMPC are covered, namely receding horizon control theory, modeling for NMPC, computational aspects of on-line optimization and application issues. The book consists of selected papers presented at the International Symposium on Nonlinear Model Predictive Control – Assessment and Future Directions, which took place from June 3 to 5, 1998, in Ascona, Switzerland. The book is geared towards researchers and practitioners in the area of control engineering and control theory. It is also suited for postgraduate students as the book contains several overview articles that give a tutorial introduction into the various aspects of nonlinear model predictive control, including systems theory, computations, modeling and applications.

Download or read book Traffic Sign Management written by Majid Khalilikhah and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This study links traffic sign visibility and legibility to quantify the effects of damage or deterioration on sign retroreflective performance. In addition, this study proposes GIS-based data integration strategies to obtain and extract climate, location, and emission data for in-service traffic signs. The proposed data integration strategy can also be used to assess all transportation infrastructures0́9 physical condition. Additionally, non-parametric machine learning methods are applied to analyze the combined GIS, Mobile LiDAR imaging, and digital photolog big data. The results are presented to identify the most important factors affecting sign visual condition, to predict traffic sign vandalism that obstructs critical messages to drivers, and to determine factors contributing to the temporary obstruction of the sign messages. The results of data analysis provide insight to inform transportation agencies in the development of sign management plans, to identify traffic signs with a higher likelihood of failure, and to schedule sign replacement.

Download or read book Traffic Data On the fly written by Alican Karaer and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Also, the performance of the model was significantly better on identifying signal crosswalks (92.5%) and uncontrolled major road crossings (96.6%) regardless of zebra or non-zebra pavement markings. The final results revealed that there are 861 mid-block, 30,784 signal, and 29,307 driveway crosswalks on the state roads in Florida. The proposed framework can be adapted in other locations where the appropriate imagery and vector data are available and is expected to contribute on the wider use of machine learning in transport policy. Finally, the fifth case study presented the literature review and the state of the art on drone utilization for traffic monitoring with the desired traffic data while providing the background on computer vision and particularly on the Multi Object Tracking (MOT) methodology with a preliminary case study to perform a real-time object detection on webcam. The provided summary of drone utilization among the U.S. transportation agencies can help stakeholders on how to benefit from UAS technology on their day-to-day work. Moreover, uniform traffic study manuals can be updated with the presented desired data analysis to help practitioners perform drone-based traffic monitoring. Furthermore, the benchmark MOT algorithms along with their data sets and performance metrics were introduced and a case study for real-time object detection was performed to pave the way for real-time naturalistic trajectory extraction and analysis. A small program was developed using python programing language, and the code was shared to apply real-time object detection on the images extracted from users' webcam. Overall findings of this dissertation depict that regardless of the imagery data source or image processing method, a comprehensive pre-processing step should be performed with a focus on the targeted end data. In addition, satellite imagery and remote sensing methods powered by GIS, provide free and exhaustive data that can be used for smarter cities. Deep learning methods have extensive power over image data; in fact, some models for certain tasks have already surpassed human performance. However, it requires serious understanding to convert commonly known neural network models to perform slightly different tasks than what they were initially trained for. Tethered drones have also been promising solutions to collect continuous and automated traffic data. However, there is a trade off since the operation is too sensitive to weather events such as wind.

Download or read book Efficient and Robust Machine Learning Methods for Challenging Traffic Video Sensing Applications written by Yifan Zhuang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of economics and technologies has promoted urbanization worldwide. Urbanization has brought great convenience to daily life. The fast construction of transportation facilities provides various means of transportation for everyday commuting. However, the growing traffic volume has threatened the existing transportation system by raising more traffic safety and congestion issues. Therefore, it is urgent and necessary to implement ITS with dynamic sensing and adjustment abilities. ITS shows great potential to improve traffic safety and efficiency, empowered by advanced IoT and AI. Within this system, the urban sensing and data analysis modules play an essential role in providing primary traffic information for follow-up works, including traffic prediction, operation optimization, and urban planning. Cameras and computer vision algorithms are the most popular toolkit in traffic sensing and analysis tasks. Deep learning-based computer vision algorithms have succeeded in multiple traffic sensing and analysis tasks, e.g., vehicle counting and crowd motion detection. The large-scale deployment of the sensor network and applications of deep learning algorithms significantly magnify previous methods' flaws, which hinder the further expansion of ITS. Firstly, the large-scale sensors and various tasks bring massive data and high workloads for data analysis on central servers. In contrast, annotated data for deep learning training in different tasks is insufficient, which leads to poor generalization when transferring to another application scenario. Additionally, traffic sensing faces adverse conditions with insufficient data and analysis qualities. This dissertation works on proposing efficient and robust machine learning methods for challenging traffic video sensing applications by presenting a systematic and practical workflow to optimize algorithm accuracy and efficiency. This dissertation first considers the high data volume challenge by designing a compression and knowledge distillation pipeline to reduce the model complexity and maintain accuracy. After applying the proposed pipeline, it is possible to further use the optimized algorithm on edge devices. This pipeline also works as the optimization foundation in the remaining works of this dissertation. Besides high data volume for analysis, insufficient training data is a considerable problem when deploying deep learning in practice. This dissertation has focused on two representative scenarios related to public safety – detecting and tracking small-scale persons in crowds and detecting rare objects in autonomous driving. Data augmentation and FSL strategies have been applied to increase the robustness of the machine learning system with limited training data. Finally, traffic sensing targets 24/7 stable operation, even in adverse conditions that reduce visibility and increase image noise with the RGB camera. Sensor fusion by combining RGB and infrared cameras is studied to improve accuracy in all light conditions. In conclusion, urbanization has simultaneously brought opportunities and challenges to the transportation system. ITS shows great potential to take this development chance and handle these challenges. This dissertation works on three data-oriented challenges and improves the accuracy and efficiency of vision-based traffic sensing algorithms. Several ITS applications are explored to demonstrate the effectiveness of the proposed methods, which achieve state-of-the-art accuracy and are far more efficient. In the future, additional research works can be explored based on this dissertation. With the continuing expansion of the sensor network, edge computing will be a more suitable system framework than cloud computing. Binary quantization and hardware-specific operator optimization can contribute to edge computing. Since data insufficiency is common in other transportation applications besides traffic detection, FSL will elevate traffic pattern forecasting and event analysis with a sequence model. For crowd monitoring, the next step will be motion prediction in bird's-eye view based on motion detection results.

Download or read book Deep Learning Methods for Automotive Radar Signal Processing written by Rodrigo Pérez González and published by Cuvillier. This book was released on 2021-06-28 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: For autonomous driving to become a reality, future sensor systems must be able to not only capture the vehicle's environment, but also to provide semantic information. In this work, deep learning methods, meant to enhance-or even replace-the classical radar signal processing chain, are developed and evaluated in the context of automotive applications. For this purpose, state of the art computer vision approaches are adapted and applied to radar signals in order to detect and classify different road users.

Download or read book Labelling Road Scenes Using Machine Learning and Stereo Vision written by Thomas Osgood and published by LAP Lambert Academic Publishing. This book was released on 2015-10-20 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Vehicles capable of sensing their surroundings are not only of interest to car manufactures for safety systems, but the underlying systems are also applicable to autonomous space exploration, military applications e.g. the DARPA challenge and fully autonomous passenger cars. The ability to autonomously detect and avoid pedestrians, for example, would be the next step in the suite of existing vision based driver assistance technologies such as road sign detection and lane departure warning systems. The main goal of this work is to explore all the tasks involved in the processing of raw sensor data into scene description which is meaningful to a computer. This starts with the selection, configuration and evaluation of current vehicle sensors. Then the processing and identification of the collected data. The project will evaluate a range of currently used techniques in the field of image processing and classification. In areas where information is currently lacking, such as a comparison between classification techniques, further investigation is carried out. Where current techniques do not provide results ideal for this application, improvements have been suggested."

Download or read book Traffic Sign Detection Using High Level Synthesis written by Sandeep Shankarappa Somanal and published by . This book was released on 2019 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays, due to increase in the number of vehicles on the street there are more road fatalities due to human errors such as not adhering to traffic signs or missing out a stop or speed limit sign. It is significant to improve technology related to road safety to decline the number of road fatalities. Advanced Driver Assistance System (ADAS) has features like collision-detection, lane-detection, traffic sign detection and recognition etc., which alerts the driver. The aim of this project is related to one of the features of an ADAS system, i.e. implementation of traffic sign detection system on Xilinx Zedboard. In real-time, the computer vision application needs to be as fast as possible. So, the image processing part of the algorithm is implemented on the Programmable Logic (PL) section of the board because of the parallel computing capability of hardware when compared to general-purpose processors. But the dashcam which streams the 1920 X 1080 image frames into IP, is driven by ARM processor available on Zynq system. So, this is kind of SW/HW co-designed system. In this project, the choice of FPGA as hardware platform is way to go because of its low-cost and low-NRE (Non-recurring engineering). Developing complex image processing application on programmable logic using HDL is traditional approach, but also time consuming. In this project, image processing application is synthesized on hardware using High Level Synthesis approach. Processing techniques like color detection, noise filtering, morphological operations and edge detection is implemented using C++ programming. RTL IP developed in this project is coded in C++ language and later synthesized using HLS. Although the scope of this project is limited to detection part due to limited resource available on PL section of the Zynq board, the recognition part can be implemented on Kintex or Virtex series. The recognition algorithm involves complex technique such as Hough line transform or circular transform which is out of scope for this project.