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 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 Deep learning based Multiple Object Tracking in Traffic Surveillance Video written by Liqiang Ding and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Multiple object tracking (MOT) is an important topic in the computer vision. One of its important applications is in traffic surveillance for examining potential risks for traffic intersections and providing analysis of road usages. In this thesis, we propose a powerful and efficient model for solving MOT problems under traffic surveillance environments. The model solves MOT problems with the strategy of tracking-by-detection, and is flexible in tracking 11 categories of common road users from various altitudes and camera pitches. Moreover, it is an end-to-end solution that removes need of any further processes. There is no manual labeling required in the initialization step and objects can be tracked regardless of their motion states, which makes it possible to be applied in a large scale. We validate our model with multiple challenging datasets and compare its performance with other state-of-art methods. The evaluation shows our model can deliver satisfying results even though a simple data association algorithm is utilized. Optical flow and discrete Kalman filter achieve competitive performances in extracting and predicting motion states of objects. However, there are not many methods available to combine them with deep learning models to solve MOT problems. Our proposed model achieves object detection with a pre-trained deep learning detector, and then performs data association based on optical flow vectors, object categories, and object spatial locations. In order to improve the accuracy, a combination of techniques such as Gaussian mixture modeling is employed. To handle occlusion and lost track problems, a Kalman filter is introduced to extrapolate the motion and spatial states of an object in the next frame, so that the model can still keep tracking for a certain number of frames. Our model recovers a tracking trajectory by connecting the tracklet to a new detection response if it has similar optical flow and the same category in a region of interest. We comprehensively examine both detection and tracking stages with multiple datasets. Our detector delivers comparable results to several state-of-art methods, but with a faster processing speed. The tracking algorithm was evaluated in a benchmark test along with a few state-of-art methods. Compared to them, our model delivers competitive accuracy scores and usually achieves the best precision scores. In addition, we assemble a novel customized traffic surveillance dataset which contains videos taken under various weather, time, and camera conditions, and qualitatively test our model against it. The results demonstrate that our model well handles crowded scenarios or partial occlusions by generating smooth and complete tracking trajectories. Using a simple yet effective data association algorithm together with a Kalman filter, it serves as a powerful solution for MOT problems." --

Download or read book Improving Pedestrian Safety Using Video Data Surrogate Safety Measures and Deep Learning written by Shile Zhang and published by . This book was released on 2021 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: The research aims to improve pedestrian safety at signalized intersections using video data, surrogate safety measures and deep learning. Machine learning (including deep learning) models are proposed for predicting pedestrians’ potentially dangerous situations. On the one hand, pedestrians’ red-light violations can expose the pedestrians to motorized traffic and pose potential threats to pedestrian safety. Thus, the prediction of pedestrians’ crossing intention during red-light signals is carried out. The pose estimation technique is used to extract features on pedestrians’ bodies. Machine learning models are used to predict pedestrians’ crossing intention at intersections’ red-light, with video data collected from signalized intersections. Multiple prediction horizons are used. On the other hand, SSMs (Surrogate Safety Measures) can be used to better investigate the mechanisms of crashes proactively compared with crash data. With the SSMs indicators, pedestrians’ near-crash events can be identified. The automated computer vision techniques such as Mask R-CNN (Region-based Convolutional Neural Network) and YOLO (You Only Look Once) are utilized to generate the features of the road users from video data. The interactions between vehicles and pedestrians are analyzed. Based on that, the prediction of pedestrians’ conflicts in time series with deep learning models is carried out at the individual-vehicle level. Besides, two SSMs indicators, PET (Post Encroachment Time) and TTC (Time to Collision), are derived from videos to label pedestrians’ near-crash events. Deep learning model such as LSTM (Long Short-term Memory) is used for modeling. To make the model more adaptive to a real-time system, the signal timing data ATSPM© (Automated Traffic Signal Performance Measures) can be used. The signal cycles that contain pedestrian phases are labeled with the SSMs indicators derived from videos and then modeled. With the above-mentioned models proposed, the decision makers can determine the possible countermeasures, or the warning strategies for drivers at intersections.

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 Data Mining of Traffic Video Sequences written by Ajay J. Joshi and published by . This book was released on 2009 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatically analyzing video data is extremely important for applications such as monitoring and data collection in transportation scenarios. Machine learning techniques are often employed in order to achieve these goals of mining traffic video to find interesting events. Typically, learning-based methods require significant amount of training data provided via human annotation. For instance, in order to provide training, a user can give the system images of a certain vehicle along with its respective annotation. The system then learns how to identify vehicles in the future - however, such systems usually need large amounts of training data and thereby cumbersome human effort. In this research, we propose a method for active l\earning in which the system interactively queries the human for annotation on the most informative instances. In this way, learning can be accomplished with lesser user effort without compromising performance. Our system is also efficient computationally, thus being feasible in real data mining tasks for traffic video sequences.

Download or read book Advances in Machine Learning and Computational Intelligence written by Srikanta Patnaik and published by Springer Nature. This book was released on 2020-07-25 with total page 853 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers selected high-quality papers presented at the International Conference on Machine Learning and Computational Intelligence (ICMLCI-2019), jointly organized by Kunming University of Science and Technology and the Interscience Research Network, Bhubaneswar, India, from April 6 to 7, 2019. Addressing virtually all aspects of intelligent systems, soft computing and machine learning, the topics covered include: prediction; data mining; information retrieval; game playing; robotics; learning methods; pattern visualization; automated knowledge acquisition; fuzzy, stochastic and probabilistic computing; neural computing; big data; social networks and applications of soft computing in various areas.

Download or read book Deep learning Based Trajectory Forecast for Safety of Intersections with Multimodal Traffic Phase II written by Abduallah Mohamed and published by . This book was released on 2020 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Computer Vision ECCV 2016 written by Bastian Leibe and published by Springer. This book was released on 2016-09-16 with total page 896 pages. Available in PDF, EPUB and Kindle. Book excerpt: The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed proceedings of the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The 415 revised papers presented were carefully reviewed and selected from 1480 submissions. The papers cover all aspects of computer vision and pattern recognition such as 3D computer vision; computational photography, sensing and display; face and gesture; low-level vision and image processing; motion and tracking; optimization methods; physics-based vision, photometry and shape-from-X; recognition: detection, categorization, indexing, matching; segmentation, grouping and shape representation; statistical methods and learning; video: events, activities and surveillance; applications. They are organized in topical sections on detection, recognition and retrieval; scene understanding; optimization; image and video processing; learning; action, activity and tracking; 3D; and 9 poster sessions.

Download or read book Evaluation of Smart Video for Transit Event Detection written by and published by . This book was released on 2009 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Reinforcement Learning Based Traffic Signal Control for Signalized Intersections written by Dunhao Zhong and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Vehicles have become an indispensable means of transportation to ensure people's travel and living materials. However, with the increasing number of vehicles, traffic congestion has become severe and caused a lot of social wealth loss. Therefore, improving the efficiency of transport management is one of the focuses of current academic circles. Among the research in transport management, traffic signal control (TSC) is an effective way to alleviate traffic congestion at signalized intersections. Existing works have successfully applied reinforcement learning (RL) techniques to achieve a higher TSC efficiency. However, previous work remains several challenges in RL-based TSC methods. First, existing studies used a single scaled reward to frame multiple objectives. Nevertheless, the single scaled reward has lower scalability to assess the controller's performance on different objectives, resulting in higher volatility on different traffic criteria. Second, adaptive traffic signal control provides dynamic traffic timing plans according to unforeseeable traffic conditions. Such characteristic prohibits applying the existing eco-driving strategies whose strategies are generated based on foreseeable and prefixed traffic timing plans. To address the challenges, in this thesis, we propose to design a new RL-TSC framework along with an eco-driving strategy to improve the TSC's efficiency on multiple objectives and further smooth the traffic flows. Moreover, to achieve effective management of the system-wide traffic flows, current researches tend to focus on the design of collaborative traffic signal control methods. However, the existing collaboration-based methods often ignore the impact of transmission delay for exchanging traffic flow information on the system. Inspired by the state-of-the-art max-pressure control in the traffic signal control area, we propose a new efficient RL-based cooperative TSC scheme by improving the reward and state representation based on the max-pressure control method and developing an agent that can address the data transmission delay issue by decreasing the discrepancy between the real-time and delayed traffic conditions. To evaluate the performance of our proposed work more accurately, in addition to the synthetic scenario, we also conducted an experiment based on the real-world traffic data recorded in the City of Toronto. We demonstrate that our method surpassed the performance of the previous traffic signal control methods through comprehensive experiments.

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 Intelligent Traffic Congestion Alleviation at Intersections Based on Deep Reinforcement Learning written by 曹藐邈 and published by . This book was released on 2021 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine learning based Vehicle Delay Prediction at Signalized Intersections written by Behnoush Garshasebi and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book AI Based Transportation Planning and Operation written by Keemin Sohn and published by MDPI. This book was released on 2021-03-29 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this Special Issue is to create an an academic platform whereby high-quality research papers are published on the applications of innovative AI algorithms to transportation planning and operation. The authors present their original research articles related to the applications of AI or machine-learning techniques to transportation planning and operation. The topics of the articles encompass traffic surveillance, traffic safety, vehicle emission reduction, congestion management, traffic speed forecasting, and ride sharing strategy.