652 research outputs found

    Computer vision based traffic monitoring system for multi-track freeways

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    Nowadays, development is synonymous with construction of infrastructure. Such road infrastructure needs constant attention in terms of traffic monitoring as even a single disaster on a major artery will disrupt the way of life. Humans cannot be expected to monitor these massive infrastructures over 24/7 and computer vision is increasingly being used to develop automated strategies to notify the human observers of any impending slowdowns and traffic bottlenecks. However, due to extreme costs associated with the current state of the art computer vision based networked monitoring systems, innovative computer vision based systems can be developed which are standalone and efficient in analyzing the traffic flow and tracking vehicles for speed detection. In this article, a traffic monitoring system is suggested that counts vehicles and tracks their speeds in realtime for multi-track freeways in Australia. Proposed algorithm uses Gaussian mixture model for detection of foreground and is capable of tracking the vehicle trajectory and extracts the useful traffic information for vehicle counting. This stationary surveillance system uses a fixed position overhead camera to monitor traffic

    Efficient resource allocation for automotive active vision systems

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    Individual mobility on roads has a noticeable impact upon peoples' lives, including traffic accidents resulting in severe, or even lethal injuries. Therefore the main goal when operating a vehicle is to safely participate in road-traffic while minimising the adverse effects on our environment. This goal is pursued by road safety measures ranging from safety-oriented road design to driver assistance systems. The latter require exteroceptive sensors to acquire information about the vehicle's current environment. In this thesis an efficient resource allocation for automotive vision systems is proposed. The notion of allocating resources implies the presence of processes that observe the whole environment and that are able to effeciently direct attentive processes. Directing attention constitutes a decision making process dependent upon the environment it operates in, the goal it pursues, and the sensor resources and computational resources it allocates. The sensor resources considered in this thesis are a subset of the multi-modal sensor system on a test vehicle provided by Audi AG, which is also used to evaluate our proposed resource allocation system. This thesis presents an original contribution in three respects. First, a system architecture designed to efficiently allocate both high-resolution sensor resources and computational expensive processes based upon low-resolution sensor data is proposed. Second, a novel method to estimate 3-D range motion, e cient scan-patterns for spin image based classifiers, and an evaluation of track-to-track fusion algorithms present contributions in the field of data processing methods. Third, a Pareto efficient multi-objective resource allocation method is formalised, implemented, and evaluated using road traffic test sequences

    Artificial intelligence enabled automatic traffic monitoring system

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    The rapid advancement in the field of machine learning and high-performance computing have highly augmented the scope of video-based traffic monitoring systems. In this study, an automatic traffic monitoring system is proposed that deploys several state-of-the-art deep learning algorithms based on the nature of traffic operation. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to track congestion, detect traffic anomalies and tabulate vehicle counts. To monitor traffic queues, this study implements a Mask region-based convolutional neural network (Mask R-CNN) that predicts congestion using pixel-level segmentation masks on classified regions of interest. Similarly, the model was used to accurately extract traffic queue-related information from infrastructure mounted video cameras. The use of infrastructure-mounted CCTV cameras for traffic anomaly detection and verification is further explored. Initially, a convolutional neural network model based on you only look once (YOLO), a popular deep learning framework for object detection and classification is deployed. The following identification model, together with a multi-object tracking system (based on intersection over union -- IOU) is used to search for and scrutinize various traffic scenes for possible anomalies. Likewise, several experiments were conducted to fine-tune the system's robustness in different environmental and traffic conditions. Some of the techniques such as bounding box suppression and adaptive thresholding were used to reduce false alarm rates and refine the robustness of the methodology developed. At each stage of our developments, a comparative analysis is conducted to evaluate the strengths and limitations of the proposed approach. Likewise, IOU tracker coupled with YOLO was used to automatically count the number of vehicles whose accuracy was later compared with a manual counting technique from CCTV video feeds. Overall, the proposed system is evaluated based on F1 and S3 performance metrics. The outcome of this study could be seamlessly integrated into traffic system such as smart traffic surveillance system, traffic volume estimation system, smart work zone management systems, etc.by Vishal MandalIncludes bibliographical reference

    A comprehensive review of vehicle detection using computer vision

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    A crucial step in designing intelligent transport systems (ITS) is vehicle detection. The challenges of vehicle detection in urban roads arise because of camera position, background variations, occlusion, multiple foreground objects as well as vehicle pose. The current study provides a synopsis of state-of-the-art vehicle detection techniques, which are categorized according to motion and appearance-based techniques starting with frame differencing and background subtraction until feature extraction, a more complicated model in comparison. The advantages and disadvantages among the techniques are also highlighted with a conclusion as to the most accurate one for vehicle detection

    Weighted Bayesian Gaussian Mixture Model for Roadside LiDAR Object Detection

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    Background modeling is widely used for intelligent surveillance systems to detect moving targets by subtracting the static background components. Most roadside LiDAR object detection methods filter out foreground points by comparing new data points to pre-trained background references based on descriptive statistics over many frames (e.g., voxel density, number of neighbors, maximum distance). However, these solutions are inefficient under heavy traffic, and parameter values are hard to transfer from one scenario to another. In early studies, the probabilistic background modeling methods widely used for the video-based system were considered unsuitable for roadside LiDAR surveillance systems due to the sparse and unstructured point cloud data. In this paper, the raw LiDAR data were transformed into a structured representation based on the elevation and azimuth value of each LiDAR point. With this high-order tensor representation, we break the barrier to allow efficient high-dimensional multivariate analysis for roadside LiDAR background modeling. The Bayesian Nonparametric (BNP) approach integrates the intensity value and 3D measurements to exploit the measurement data using 3D and intensity info entirely. The proposed method was compared against two state-of-the-art roadside LiDAR background models, computer vision benchmark, and deep learning baselines, evaluated at point, object, and path levels under heavy traffic and challenging weather. This multimodal Weighted Bayesian Gaussian Mixture Model (GMM) can handle dynamic backgrounds with noisy measurements and substantially enhances the infrastructure-based LiDAR object detection, whereby various 3D modeling for smart city applications could be created

    Vision-based Detection, Tracking and Classification of Vehicles using Stable Features with Automatic Camera Calibration

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    A method is presented for segmenting and tracking vehicles on highways using a camera that is relatively low to the ground. At such low angles, 3D perspective effects cause significant appearance changes over time, as well as severe occlusions by vehicles in neighboring lanes. Traditional approaches to occlusion reasoning assume that the vehicles initially appear well-separated in the image, but in our sequences it is not uncommon for vehicles to enter the scene partially occluded and remain so throughout. By utilizing a 3D perspective mapping from the scene to the image, along with a plumb line projection, a subset of features is identified whose 3D coordinates can be accurately estimated. These features are then grouped to yield the number and locations of the vehicles, and standard feature tracking is used to maintain the locations of the vehicles over time. Additional features are then assigned to these groups and used to classify vehicles as cars or trucks. The technique uses a single grayscale camera beside the road, processes image frames incrementally, works in real time, and produces vehicle counts with over 90% accuracy on challenging sequences. Adverse weather conditions are handled by augmenting feature tracking with a boosted cascade vehicle detector (BCVD). To overcome the need of manual camera calibration, an algorithm is presented which uses BCVD to calibrate the camera automatically without relying on any scene-specific image features such as road lane markings

    Efficient Min-cost Flow Tracking with Bounded Memory and Computation

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    This thesis is a contribution to solving multi-target tracking in an optimal fashion for real-time demanding computer vision applications. We introduce a challenging benchmark, recorded with our autonomous driving platform AnnieWAY. Three main challenges of tracking are addressed: Solving the data association (min-cost flow) problem faster than standard solvers, extending this approach to an online setting, and making it real-time capable by a tight approximation of the optimal solution

    Object Tracking

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    Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

    Advanced vision based vehicle classification for traffic surveillance system using neural networks

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    Master's thesis in Cybernetics and signal processingThis master thesis focus on traffic monitoring, which are of importance to fulfill planning and traffic management of road networks. An important requirement is data interpretation accuracy to provide adequate characteristic data from the acquired vision-data. A vision-based system has been developed, using new methods and technologies to achieve an automated traffic monitoring system, without the use of additional sensors. The thesis is based upon Erik Sudland’s master thesis from 2016, which investigated available litterateur containing adequate algorithms for traffic monitoring. However in the current master thesis, methods have been further analyzed and experimentally optimized on vision-data from real traffic situations. In addition, a new classification method based upon neural networks has been implemented and verified with successful result
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