73 research outputs found

    Cyclist Detection, Tracking, and Trajectory Analysis in Urban Traffic Video Data

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    The major objective of this thesis work is examining computer vision and machine learning detection methods, tracking algorithms and trajectory analysis for cyclists in traffic video data and developing an efficient system for cyclist counting. Due to the growing number of cyclist accidents on urban roads, methods for collecting information on cyclists are of significant importance to the Department of Transportation. The collected information provides insights into solving critical problems related to transportation planning, implementing safety countermeasures, and managing traffic flow efficiently. Intelligent Transportation System (ITS) employs automated tools to collect traffic information from traffic video data. In comparison to other road users, such as cars and pedestrians, the automated cyclist data collection is relatively a new research area. In this work, a vision-based method for gathering cyclist count data at intersections and road segments is developed. First, we develop methodology for an efficient detection and tracking of cyclists. The combination of classification features along with motion based properties are evaluated to detect cyclists in the test video data. A Convolutional Neural Network (CNN) based detector called You Only Look Once (YOLO) is implemented to increase the detection accuracy. In the next step, the detection results are fed into a tracker which is implemented based on the Kernelized Correlation Filters (KCF) which in cooperation with the bipartite graph matching algorithm allows to track multiple cyclists, concurrently. Then, a trajectory rebuilding method and a trajectory comparison model are applied to refine the accuracy of tracking and counting. The trajectory comparison is performed based on semantic similarity approach. The proposed counting method is the first cyclist counting method that has the ability to count cyclists under different movement patterns. The trajectory data obtained can be further utilized for cyclist behavioral modeling and safety analysis

    Multiple Road Users Detection And Tracking System In Urban Mixed Traffic Scenes

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    Video analytic technology in traffic control and monitoring is getting more attention in recent years. This is because video analytic technology can perform traffic surveillance to extract traffic information such as vehicle counting and classification from video sequence. Large amount of road user data can be generated from video and this data would benefit for traffic planner. This provides the impact of video analytic in traffic surveillance. However, multiple road users tracking in urban traffic remains challenging because of large variation of road user appearance. To overcome the problems of multiple object tracking in mixed urban traffic, which are mis-detection, frequent ID switches and mis-classification, a system known as City Tracker, which incorporates Maximum Likelihood Estimation (MLE), YOLOv3 and DeepSORT is proposed in mixed urban traffic. City Tracker predicts the potential bounding box coordinates from the result of YOLOv3 and DeepSORT, then matches with the latest actual bounding box to overcome the mis-detection and frequent identity switch. On the other hand, MLE provides trajectory-based classification to solve mis-classification. This solution is tested with Urban Tracker dataset based on detection and tracking performance. The performance evaluations show that implementation of City Tracker increases Multiple Object Tracking Accuracy (MOTA) from 0.3503 to 0.3793 (8.28%) and Multiple Object Tracking Precision (MOTP) from 0.6245 to 0.6442 (3.15%) which are calculated from Precision and Recall as the evaluation metrics. MLE improves Recall from 0.7032 to 0.7838 (11.46%) and Precision from 0.7214 to 0.8334 (15.53%) in classification performance, which is better than conventional YOLOv3 and DeepSORT that do not consider City Tracker

    Parallel Tracking and Mapping for Manipulation Applications with Golem Krang

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    Implementing a simultaneous localization and mapping system and an image semantic segmentation method on a mobile manipulation. The application of the SLAM is working towards navigating among obstacles in unknown environments. The object detection method will be integrated for future manipulation tasks such as grasping. This work will be demonstrated on a real robotics hardware system in the lab.Outgoin

    Detecting and tracking people in real-time

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    The problem of detecting and tracking people in images and video has been the subject of a great deal of research, but remains a challenging task. Being able to detect and track people would have an impact in a number of fields, such as driverless vehicles, automated surveillance, and human-computer interaction. The difficulties that must be overcome include coping with variations in appearance between different people, changes in lighting, and the ability to detect people across multiple scales. As well as having high accuracy, it is desirable for a technique to evaluate an image with low latency between receiving the image and producing a result. This thesis explores methods for detecting and tracking people in images and video. Techniques are implemented on a desktop computer, with an emphasis on low latency. The problem of detection is examined first. The well established integral channel features detector is introduced and reimplemented, and various novelties are implemented in regards to the features used by the detector. Results are given to quantify the accuracy and the speed of the developed detectors on the INRIA person dataset. The method is further extended by examining the prospect of using multiple classifiers in conjunction. It is shown that using a classifier with a version of the same classifier reflected in the vertical axis can improve performance. A novel method for clustering images of people to find modes of appearance is also presented. This involves using boosting classifiers to map a set of images to vectors, to which K-means clustering is applied. Boosting classifiers are then trained on these clustered datasets to create sets of multiple classifiers, and it is demonstrated that these sets of classifiers can be evaluated on images with only a small increase in the running time over single classifiers. The problem of single target tracking is addressed using the mean shift algorithm. Mean shift tracking works by finding the best colour match for a target from frame to frame. A novel form of mean shift tracking through scale is developed, and the problem of multiple target tracking is addressed by using boosting classifiers in conjunction with Kalman filters. Tests are carried out on the CAVIAR dataset, which gives representative examples of surveillance scenarios, to show the performance of the proposed approaches.Open Acces

    Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review

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    Motorcycles are Vulnerable Road Users (VRU) and as such, in addition to bicycles and pedestrians, they are the traffic actors most affected by accidents in urban areas. Automatic video processing for urban surveillance cameras has the potential to effectively detect and track these road users. The present review focuses on algorithms used for detection and tracking of motorcycles, using the surveillance infrastructure provided by CCTV cameras. Given the importance of results achieved by Deep Learning theory in the field of computer vision, the use of such techniques for detection and tracking of motorcycles is also reviewed. The paper ends by describing the performance measures generally used, publicly available datasets (introducing the Urban Motorbike Dataset (UMD) with quantitative evaluation results for different detectors), discussing the challenges ahead and presenting a set of conclusions with proposed future work in this evolving area

    Extended Object Tracking: Introduction, Overview and Applications

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    This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.Comment: 30 pages, 19 figure

    Motorcycle detection for ADAS through camera and V2V communication, a comparative analysis of two modern technologies

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    Motorcycles are one of the most dangerous means of transportation. Its death toll is higher than in others, due to the inherent vulnerability of motorcycle drivers. The latest strategies in Advanced Driving Assistance Systems (ADAS) are trying to mitigate this problem by applying the advances of modern technologies to the road transport. This paper presents two different approaches on motorcycle protection, based on two of the most modern available technologies in ADAS, i.e. Computer Vision and Vehicle to Vehicle Communication (V2V). The first approach is based on data fusion of Laser Scanner and Computer Vision, providing accurate obstacle detection and localization based on laser scanner, and obstacle classification using computer vision and laser. The second approach is based on ad-hoc V2V technology and provides detection in case of occlusion for visual sensors. Both technologies have been tested in the presented work, and a performance comparison is given. Tests performed in different driving situations allows to measure the performance of every algorithm and the limitations of each of them based on empirical and scientific foundations. The conclusions of the presented work help foster of expert systems in the automotive sector by providing further discussion of the viability and impact from each of these systems in real scenarios

    Video-Based Environment Perception for Automated Driving using Deep Neural Networks

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    Automatisierte Fahrzeuge benötigen eine hochgenaue Umfeldwahrnehmung, um sicher und komfortabel zu fahren. Gleichzeitig müssen die Perzeptionsalgorithmen mit der verfügbaren Rechenleistung die Echtzeitanforderungen der Anwendung erfüllen. Kamerabilder stellen eine sehr wichtige Informationsquelle für automatisierte Fahrzeuge dar. Sie beinhalten mehr Details als Daten von anderen Sensoren wie Lidar oder Radar und sind oft vergleichsweise günstig. Damit ist es möglich, ein automatisiertes Fahrzeug mit einem Surround-View Sensor-Setup auszustatten, ohne die Gesamtkosten zu stark zu erhöhen. In dieser Arbeit präsentieren wir einen effizienten und genauen Ansatz zur videobasierten Umfeldwahrnehmung für automatisierte Fahrzeuge. Er basiert auf Deep Learning und löst die Probleme der Objekterkennung, Objektverfolgung und der semantischen Segmentierung von Kamerabildern. Wir schlagen zunächst eine schnelle CNN-Architektur zur gleichzeitigen Objekterkennung und semantischen Segmentierung vor. Diese Architektur ist skalierbar, so dass Genauigkeit leicht gegen Rechenzeit eingetauscht werden kann, indem ein einziger Skalierungsfaktor geändert wird. Wir modifizieren diese Architektur daraufhin, um Embedding-Vektoren für jedes erkannte Objekt vorherzusagen. Diese Embedding-Vektoren werden als Assoziationsmetrik bei der Objektverfolgung eingesetzt. Sie werden auch für einen neuartigen Algorithmus zur Non-Maximum Suppression eingesetzt, den wir FeatureNMS nennen. FeatureNMS kann in belebten Szenen, in denen die Annahmen des klassischen NMS-Algorithmus nicht zutreffen, einen höheren Recall erzielen. Wir erweitern anschlie{\ss}end unsere CNN-Architektur für Einzelbilder zu einer Mehrbild-Architektur, welche zwei aufeinanderfolgende Videobilder als Eingabe entgegen nimmt. Die Mehrbild-Architektur schätzt den optischen Fluss zwischen beiden Videobildern innerhalb des künstlichen neuronalen Netzwerks. Dies ermöglicht es, einen Verschiebungsvektor zwischen den Videobildern für jedes detektierte Objekt zu schätzen. Diese Verschiebungsvektoren werden ebenfalls als Assoziationsmetrik bei der Objektverfolgung eingesetzt. Zuletzt präsentieren wir einen einfachen Tracking-by-Detection-Ansatz, der wenig Rechenleistung erfordert. Er benötigt einen starken Objektdetektor und stützt sich auf die Embedding- und Verschiebungsvektoren, die von unserer CNN-Architektur geschätzt werden. Der hohe Recall des Objektdetektors führt zu einer häufigen Detektion der verfolgten Objekte. Unsere diskriminativen Assoziationsmetriken, die auf den Embedding- und Verschiebungsvektoren basieren, ermöglichen eine zuverlässige Zuordnung von neuen Detektionen zu bestehenden Tracks. Diese beiden Bestandteile erlauben es, ein einfaches Bewegungsmodell mit Annahme einer konstanten Geschwindigkeit und einem Kalman-Filter zu verwenden. Die von uns vorgestellten Methoden zur videobasierten Umfeldwahrnehmung erreichen gute Resultate auf den herausfordernden Cityscapes- und BDD100K-Datensätzen. Gleichzeitig sind sie recheneffizient und können die Echtzeitanforderungen der Anwendung erfüllen. Wir verwenden die vorgeschlagene Architektur erfolgreich innerhalb des Wahrnehmungs-Moduls eines automatisierten Versuchsfahrzeugs. Hier hat sie sich in der Praxis bewähren können

    A Novel Collision Avoidance System for a Bicycle

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    This project focuses on development of a sensing and estimation system for a bicycle to accurately detect and track vehicles for two types of car-bicycle collisions. The two types of collisions considered are collisions from rear vehicles and collisions from right-turning vehicles at a traffic intersection. The collision detection system on a bicycle is required to be inexpensive, small and lightweight. Sensors that meet these constraints are utilized.To monitor side vehicles and detect danger from a right-turning car, a custom sonar sensor is developed. It consists of one ultrasonic transmitter and two receivers from which both the lateral distance and the orientation of the car can be obtained. A Kalman Filter-based vehicle tracking system that utilizes this custom sonar sensor is developed and implemented. Experimental results show that it can reliably differentiate between straight driving and turning cars. A warning can be provided in time to prevent a collision. For tracking rear vehicles, an inexpensive single-beam laser sensor is mounted on a rotationally controlled platform. The rotational orientation of the laser sensor needs to be actively controlled in real-time in order to continue to focus on a rear vehicle, as the vehicle’s lateral and longitudinal distances change. This tracking problem requires controlling the real-time angular position of the laser sensor without knowing the future trajectory of the vehicle. The challenge is addressed using a novel receding horizon framework for active control and an interacting multiple model framework for estimation. The features and benefits of this active sensing system are illustrated first using simulation results. Then, extensive experimental results are presented using an instrumented bicycle to show the performance of the system in detecting and tracking rear vehicles during both straight and turning maneuvers
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