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Moving Vehicle Detection In Traffic Surveillance Using Mamoving Vehicle Detection In Traffic Surveillance Using Machine Learning Techniqueschine Learning Techniques
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Book Synopsis Moving Vehicle Detection in Traffic Surveillance Using MaMoving Vehicle Detection in Traffic Surveillance Using Machine Learning Techniqueschine Learning Techniques by : Smithaj A
Download or read book Moving Vehicle Detection in Traffic Surveillance Using MaMoving Vehicle Detection in Traffic Surveillance Using Machine Learning Techniqueschine Learning Techniques written by Smithaj A and published by Mohd Abdul Hafi. This book was released on 2024-02-24 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent Transport Systems (ITS) is the concept for the construction of a Transport Infrastructure that involves a driven data, telecommunications network for consumers, roads and automobiles. Video Detection is most widespread used in the Intelligent Transportation System (ITS) as it plays a key role in Transportation System. Dynamic modifications of background images due to environment, lightening, shadows make it inconvenient in detecting and tracking moving vehicles from the videos. Therefore, the moving background objects, brightness variation and vehicle adhesion create several challenges in the detection of moving vehicle. To minimize the problem, automatic moving vehicle detection is discussed in this thesis. This chapter consists of background of ITS system, Moving Vehicle System, Tracking Algorithms and applications. It also discusses the problems in Vehicle Detection System, followed by the Research Objective and Thesis Organization.
Book Synopsis Video Based Machine Learning for Traffic Intersections by : Tania Banerjee
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
Book Synopsis Vehicle Detection and Tracking in Highway Surveillance Videos by : Birgi Tamersoy
Download or read book Vehicle Detection and Tracking in Highway Surveillance Videos written by Birgi Tamersoy and published by . This book was released on 2009 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: We present a novel approach for vehicle detection and tracking in highway surveillance videos. This method incorporates well-studied computer vision and machine learning techniques to form an unsupervised system, where vehicles are automatically "learned" from video sequences. First an enhanced adaptive background mixture model is used to identify positive and negative examples. Then a video-specific classifier is trained with these examples. Both the background model and the trained classifier are used in conjunction to detect vehicles in a frame. Tracking is achieved by a simplified multi-hypotheses approach. An over-complete set of tracks is created considering every observation within a time interval. As needed hypothesized detections are generated to force continuous tracks. Finally, a scoring function is used to separate the valid tracks in the over-complete set. The proposed detection and tracking algorithm is tested in a challenging application; vehicle counting. Our method achieved very accurate results in three traffic surveillance videos that are significantly different in terms of view-point, quality and clutter.
Book Synopsis Road User Detection and Analysis in Traffic Surveillance Videos by : Jinling Li
Download or read book Road User Detection and Analysis in Traffic Surveillance Videos written by Jinling Li and published by . This book was released on 2014 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: Road user data collection and behaviour analysis has been an active research topic in the last decade. Automated solutions can be achieved based on video analysis with computer vision techniques. In this thesis, we propose a method to estimate traffic objects' locations with state-of-the-art vision features and learning models. Our focus is put on the applications of cyclist's helmet recognition and 3D vehicle localization. With limited human labelling, we adopt a semi-supervised learning process: tri-training with views of shapes and motion flow for vehicle detection. Experiments are conducted in real-world traffic surveillance videos.
Book Synopsis Video Based Machine Learning for Traffic Intersections by : Tania Banerjee (Computer scientist)
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"--
Book Synopsis Moving Objects Detection Using Machine Learning by : Navneet Ghedia
Download or read book Moving Objects Detection Using Machine Learning written by Navneet Ghedia and published by Springer Nature. This book was released on 2022-01-01 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows how machine learning can detect moving objects in a digital video stream. The authors present different background subtraction approaches, foreground segmentation, and object tracking approaches to accomplish this. They also propose an algorithm that considers a multimodal background subtraction approach that can handle a dynamic background and different constraints. The authors show how the proposed algorithm is able to detect and track 2D & 3D objects in monocular sequences for both indoor and outdoor surveillance environments and at the same time, also able to work satisfactorily in a dynamic background and with challenging constraints. In addition, the shows how the proposed algorithm makes use of parameter optimization and adaptive threshold techniques as intrinsic improvements of the Gaussian Mixture Model. The presented system in the book is also able to handle partial occlusion during object detection and tracking. All the presented work and evaluations were carried out in offline processing with the computation done by a single laptop computer with MATLAB serving as software environment.
Book Synopsis Moving Vehicle Detection and Tracking System by : Xin Li
Download or read book Moving Vehicle Detection and Tracking System written by Xin Li and published by . This book was released on 2004 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Deep Learning based Vehicle Detection in Aerial Imagery by : Sommer, Lars Wilko
Download or read book Deep Learning based Vehicle Detection in Aerial Imagery written by Sommer, Lars Wilko and published by KIT Scientific Publishing. This book was released on 2022-02-09 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes a novel deep learning based detection method, focusing on vehicle detection in aerial imagery recorded in top view. The base detection framework is extended by two novel components to improve the detection accuracy by enhancing the contextual and semantical content of the employed feature representation. To reduce the inference time, a lightweight CNN architecture is proposed as base architecture and a novel module that restricts the search area is introduced.
Book Synopsis EFFECTIVE METHODOLOGY FOR VISU by : Hon-Seng Lai
Download or read book EFFECTIVE METHODOLOGY FOR VISU written by Hon-Seng Lai and published by Open Dissertation Press. This book was released on 2017-01-27 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "An Effective Methodology for Visual Traffic Surveillance" by 賴翰笙, Hon-seng, Lai, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled An Effective Methodology for Visual Traffic Surveillance submitted by LAI Hon Seng for the degree of Doctor of Philosophy at the University of Hong Kong in January 2000 This thesis presents a methodology for Automatic Visual Traffic Surveillance. The methodology has been developed upon the concept of Unconstrained Detection Regions, which enables it to perform multiple vehicle detection, tracking and traffic parameter estimation in real time. The goal of this research was to develop a suitable methodology for automatic visual traffic surveillance to perform multiple vehicle detection, tracking and traffic parameter estimation in real time as well as being able to tackle scene changes automatically, and detect and handle vehicle occlusion. The proposed methodology is able to first, tackle unpredictable outdoor environment changes, noises, camera actions and vibration automatically based on several novel algorithms. Second, detect vehicle occlusion by monitoring the changes in vehicle model dimensions and handle occlusion using a novel algorithm, so that vehicle can be tracked individually. Third, estimate traffic parameters of individual vehicles or road conditions. Fourth, achieve real time response by employing efficient algorithm to reduce the computation requirement. It has been tested on a number of freeway surveillance image sequences and has shown to be successful in performing the above correctly.In essence, the methodology employs a modular architecture, which consists of four modules: (1) feature preserving noise filtering; (2) vehicle extraction; (3) vehicle tracking; and (4) traffic information estimation, each of which was studied and novel algorithms were proposed to tackle their respective problems. Specifically, to remove noise in digital images and to minimize the feature degradation, a feature preserving noise filtering method has been developed. In it, pixels are classified into either corrupted or uncorrupted and only those corrupted pixels are filtered. Our evaluation has shown that the new method can remove noise effectively, preserve image features, has faster processing speed and can be applied iteratively. In vehicle extraction, a background subtraction approach has been considered, from which, vehicles are extracted by subtracting a stationary background from the image sequence. A scoreboard-based background estimation method has been developed to record the variation of each pixel for selecting different estimation algorithms. Our evaluation has shown that it is fast and accurate. From the background, road parameters are extracted automatically using an edge-based approach in conjunction with two edge discrimination heuristics to determine the road lanes, centerline and lane direction. Our evaluation indicated that it is fast, automatic and can work with straight roads with multiple lanes. Complex vehicle occlusion that appears in visual surveillance is tackled by a generalized deformable modeling method and a vehicle occlusion detection and handling method. In principle, a vehicle is fitted by a 2D projection of a 3D cuboid wire frame with parameterized vertices. By monitoring the changes in the model dimensions and its Area ratio, occlusion is detected successfully every time, and the trajectory is deduced. When occlusion occurs, vehicles are tracked separately byreplicating the vehicle trajectory or splitting the occluded vehicle model. Trials on real-w
Book Synopsis Sensing Vehicle Conditions for Detecting Driving Behaviors by : Jiadi Yu
Download or read book Sensing Vehicle Conditions for Detecting Driving Behaviors written by Jiadi Yu and published by Springer. This book was released on 2018-04-18 with total page 81 pages. Available in PDF, EPUB and Kindle. Book excerpt: This SpringerBrief begins by introducing the concept of smartphone sensing and summarizing the main tasks of applying smartphone sensing in vehicles. Chapter 2 describes the vehicle dynamics sensing model that exploits the raw data of motion sensors (i.e., accelerometer and gyroscope) to give the dynamic of vehicles, including stopping, turning, changing lanes, driving on uneven road, etc. Chapter 3 detects the abnormal driving behaviors based on sensing vehicle dynamics. Specifically, this brief proposes a machine learning-based fine-grained abnormal driving behavior detection and identification system, D3, to perform real-time high-accurate abnormal driving behaviors monitoring using the built-in motion sensors in smartphones. As more vehicles taking part in the transportation system in recent years, driving or taking vehicles have become an inseparable part of our daily life. However, increasing vehicles on the roads bring more traffic issues including crashes and congestions, which make it necessary to sense vehicle dynamics and detect driving behaviors for drivers. For example, sensing lane information of vehicles in real time can be assisted with the navigators to avoid unnecessary detours, and acquiring instant vehicle speed is desirable to many important vehicular applications. Moreover, if the driving behaviors of drivers, like inattentive and drunk driver, can be detected and warned in time, a large part of traffic accidents can be prevented. However, for sensing vehicle dynamics and detecting driving behaviors, traditional approaches are grounded on the built-in infrastructure in vehicles such as infrared sensors and radars, or additional hardware like EEG devices and alcohol sensors, which involves high cost. The authors illustrate that smartphone sensing technology, which involves sensors embedded in smartphones (including the accelerometer, gyroscope, speaker, microphone, etc.), can be applied in sensing vehicle dynamics and driving behaviors. Chapter 4 exploits the feasibility to recognize abnormal driving events of drivers at early stage. Specifically, the authors develop an Early Recognition system, ER, which recognize inattentive driving events at an early stage and alert drivers timely leveraging built-in audio devices on smartphones. An overview of the state-of-the-art research is presented in chapter 5. Finally, the conclusions and future directions are provided in Chapter 6.
Book Synopsis FAST PROBABILISTIC METHOD FOR by : Wai-Sing Boris Yiu
Download or read book FAST PROBABILISTIC METHOD FOR written by Wai-Sing Boris Yiu and published by Open Dissertation Press. This book was released on 2017-01-26 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "A Fast Probabilistic Method for Vehicle Detection and Tracking With an Explicit Contour Model" by Wai-sing, Boris, Yiu, 姚維勝, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled AFastProbabilisticMethodforVehicleDetectionandTracking withanExplicitContourModel submitted by YIUWaiSing, Boris for the degree of Master of Philosophy at the University of Hong Kong in August 2005 Autonomous traffic surveillance systems have to detect and track every vehicle within their fields of view in real time. One major challenge for such systems is to handle different views of various classes of vehicles. This is not a trivial problem, as the appearances of dif- ferent vehicles may look substantially different in an image, and simply stating presence is not sufficient. Such systems require accurate vehicle boundaries in the images for construct- ing an informative, distinctive and concise model for a vehicle, and for providing a strong cue for locating the vehicle during visual tracking. The systems should, therefore, detect and recognise the boundary of each vehicle. Existing methods either make restrictive assumptions on the scene or focus on a par- ticular class of vehicles in a manner that cannot be extended to a wide variety of vehicles without consuming too much computing power. In this thesis, a generic '2.5D' shape model is introduced to describe the general shapes of vehicles, and used for detecting and tracking various classes of vehicles efficiently. The proposed model encompasses most approximated 2D projections of a 3D cuboid in variable dimensions. Based on such a generic model, a probabilistic template fitting framework is developed to determine the best contour of a po- tential vehicle in images using an efficient dynamic programming algorithm. This allows traffic surveillance systems to detect various classes of vehicles efficiently. A track is then initiated for every detected vehicle automatically. To handle the image shape deformation of the vehicle during tracking, the generic 2.5D shape model is further extended to explicitly describe how the image shape of a target vehicle would vary with its motion. Such an enhanced shape model consists of a concise set of parameters that offers a reasonable level of detail for real-time surveillance applications. This low dimensional model can be integrated into any Bayesian tracking framework, and the resultant tracking algorithm can predict the next state of a target more accurately and thus make more relevant measurements from the images. The shape model also relates the shape to the velocity of the target to improve robustness by monitoring any state inconsistency. The method was evaluated on various traffic videos, and it worked effectively in realistic conditions, including curved and inclined roads as well as straight roads, and achieved good accuracy and fast performance. The approach of explicitly modelling shape deformation in a low-dimensional model was shown to be effective in a real-time surveillance system. DOI: 10.5353/th_b3505717 Subjects: Computer vision Image processing Automobiles - Tracking Bayesian statistical decision theory Algorithms
Book Synopsis Traffic Surveillance by Wireless Sensor Networks by : Sing Yiu Cheung
Download or read book Traffic Surveillance by Wireless Sensor Networks written by Sing Yiu Cheung and published by . This book was released on 2008 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Deep-learning-based Multiple Object Tracking in Traffic Surveillance Video by : Liqiang Ding
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." --
Book Synopsis Movement Detection in Outdoor Scenes for Traffic Monitoring by : Yi Liu
Download or read book Movement Detection in Outdoor Scenes for Traffic Monitoring written by Yi Liu and published by . This book was released on 2004 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Real-time Video Analytics Empowered by Machine Learning and Edge Computing for Smart Transportation Applications by : Ruimin Ke
Download or read book Real-time Video Analytics Empowered by Machine Learning and Edge Computing for Smart Transportation Applications written by Ruimin Ke and published by . This book was released on 2020 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traffic cameras have the properties of being cost-effective, information-rich, and widely deployed, which are filling up a big gap in today0́9s traffic sensor needs. With the recent progress in traffic operations, information technology, and computer vision, traffic video analytics is driving a broad range of smart city applications with great potential to benefit future transportation and infrastructure systems. Most such applications, e.g., smart traffic surveillance and autonomous driving, require not only high intelligence but also real-time processing capability. Real-time video analytics is well-believed to be one of the most challenging yet most powerful applications for smart cities. It is often bottlenecked by the large volume of video data, high computational cost, and limited data communication bandwidth. This dissertation explores general guidelines and new traffic video analytical methods and systems towards high intelligence and real-time operations for roadway transportation. The designs focus on both the algorithm level and the application system level. On the one hand, lightweight methods are devised based on machine learning techniques and transportation domain knowledge for high smartness, accuracy, and efficiency in specific traffic scenarios. On the other hand, system architectures are developed by leveraging the power of edge computing so that we can split the computational workload between the centralized servers and local Internet-of-Things (IoT) devices for the purpose of system performance optimization. The traffic analytics products and findings in this dissertation can be applied to three transportation-related scenarios with different properties regarding video data collection and processing: (1) traffic surveillance, (2) vehicle onboard sensing, and (3) unmanned aerial vehicle (UAV) sensing. Correspondingly, they apply to three key components of modern intelligent transportation systems (ITS), i.e., smart infrastructures, intelligent vehicle, and aerial surveillance for road traffic. These components possess unique characteristics that can be utilized for video analytics, yet with different challenges to address. To this end, the dissertation proposes algorithms, frameworks, and field implementation examples of how to design and evaluate traffic video analytics systems for smart transportation applications towards high intelligence and efficiency. Experiments were conducted with real-world datasets and tests in a variety of scenarios. This dissertation is among the first efforts in developing edge computing applications for transportation and in exploring UAV sensing for traffic flow.
Book Synopsis Occlusion Robust and Environment Insensitive Algorithm for Vehicle Detection and Tracking Using Surveillance Video Cameras by : Yinhai Wang
Download or read book Occlusion Robust and Environment Insensitive Algorithm for Vehicle Detection and Tracking Using Surveillance Video Cameras written by Yinhai Wang and published by . This book was released on 2008 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Vehicle Detection and Classification in 1-M Resolution Imagery by : Gaurav Sharma
Download or read book Vehicle Detection and Classification in 1-M Resolution Imagery written by Gaurav Sharma and published by . This book was released on 2002 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt:
