1,123 research outputs found

    A Joint 3D-2D based Method for Free Space Detection on Roads

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    In this paper, we address the problem of road segmentation and free space detection in the context of autonomous driving. Traditional methods either use 3-dimensional (3D) cues such as point clouds obtained from LIDAR, RADAR or stereo cameras or 2-dimensional (2D) cues such as lane markings, road boundaries and object detection. Typical 3D point clouds do not have enough resolution to detect fine differences in heights such as between road and pavement. Image based 2D cues fail when encountering uneven road textures such as due to shadows, potholes, lane markings or road restoration. We propose a novel free road space detection technique combining both 2D and 3D cues. In particular, we use CNN based road segmentation from 2D images and plane/box fitting on sparse depth data obtained from SLAM as priors to formulate an energy minimization using conditional random field (CRF), for road pixels classification. While the CNN learns the road texture and is unaffected by depth boundaries, the 3D information helps in overcoming texture based classification failures. Finally, we use the obtained road segmentation with the 3D depth data from monocular SLAM to detect the free space for the navigation purposes. Our experiments on KITTI odometry dataset, Camvid dataset, as well as videos captured by us, validate the superiority of the proposed approach over the state of the art.Comment: Accepted for publication at IEEE WACV 201

    Visual Analysis of Robot and Animal Colonies

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    Graphical & digital media application

    B2B2: LiDAR 2D Mapping Rover

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    Autonomous machines are becoming more popular and useful with even self-driving cars being a thing of the present. Most of these machines navigate using cameras and LiDAR which does not detect glass, therefore the machines give misleading results when objects and obstacles are transparent to the wavelengths of the light used. This is problematic in modern building floor plans with glass walls. A solution is to build a ROS system that fuses ultrasonic sensors with LiDAR sensors in order for a robot to navigate in a building that has glass walls. Using both sensors, the final product is a robot that creates a 2D map using Simultaneous Localization and Mapping (SLAM) as well as other pertinent Robotics Operating Systems (ROS) packages. This map enables any mobile robot to pathplan from point A to B on the now created 2D floor plan that incorporates glass and non-glass obstacles. This saves time and energy when compared to a robot that moves from point A to B that has to continuously change paths in the presence of obstacles

    Lime: Data Lineage in the Malicious Environment

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    Intentional or unintentional leakage of confidential data is undoubtedly one of the most severe security threats that organizations face in the digital era. The threat now extends to our personal lives: a plethora of personal information is available to social networks and smartphone providers and is indirectly transferred to untrustworthy third party and fourth party applications. In this work, we present a generic data lineage framework LIME for data flow across multiple entities that take two characteristic, principal roles (i.e., owner and consumer). We define the exact security guarantees required by such a data lineage mechanism toward identification of a guilty entity, and identify the simplifying non repudiation and honesty assumptions. We then develop and analyze a novel accountable data transfer protocol between two entities within a malicious environment by building upon oblivious transfer, robust watermarking, and signature primitives. Finally, we perform an experimental evaluation to demonstrate the practicality of our protocol

    Online tracking of ants based on deep association metrics: method, dataset and evaluation

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    Tracking movement of insects in a social group (such as ants) is challenging, because the individuals are not only similar in appearance but also likely to perform intensive body contact and sudden movement adjustment (start/stop, direction changes). To address this challenge, we introduce an online multi-object tracking framework that combines both the motion and appearance information of ants. We obtain the appearance descriptors by using the ResNet model for offline training on a small (N=50) sample dataset. For online association, a cosine similarity metric computes the matching degree between historical appearance sequences of the trajectory and the current detection. We validate our method in both indoor (lab setup) and outdoor video sequences. The results show that our model obtains 99.3% ± 0.5% MOTA and 91.9% ± 2.1% MOTP across 24,050 testing samples in five indoor sequences, with real-time tracking performance. In an outdoor sequence, we achieve 99.3% MOTA and 92.9% MOTP across 22,041 testing samples. The datasets and code are made publicly available for future research in relevant domains

    Modeling people flow in buildings using edge and cloud computing

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    In recent years, significant progress has been made in computer vision regarding object detection and tracking which has allowed the emergence of various applications. These often focus on identifying and tracking people in different environments such as buildings. Detecting people allows us to get a more comprehensive view of people flow as traditional IoT data from elevators cannot track individual people and their trajectories. In this thesis, we concentrate on people detection in elevator lobbies which we can use to improve the efficiency of the elevators and the convenience of the building. We compare the performance and speed of various object detection algorithms. Additionally, we research an edge device's capability to run an object detection model on multiple cameras and whether a single device can cover the target building. We were able to train an object detection algorithm suitable for our application. This allowed accurate people detection that can be used for people counting. We found that out of the three object detection algorithms we trained, YOLOv3 was the only one capable of generalizing to unseen environments, which is essential for general purpose application. The performances of the other two models (SSD and Faster R-CNN) were poor in terms of either accuracy or speed. Based on these, we chose to deploy YOLOv3 to the edge device. We found that the edge device's inference time is linearly dependent on the number of cameras. Therefore, we can conclude that one edge device should be sufficient for our target building, allowing two cameras for each floor. We also demonstrated that the edge device allows easy addition of an object tracking layer, which is required for the solution to work in a real-life office building

    Data Hiding and Its Applications

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    Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others

    Ehmi: Review and guidelines for deployment on autonomous vehicles

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    Human-machine interaction is an active area of research due to the rapid development of autonomous systems and the need for communication. This review provides further insight into the specific issue of the information flow between pedestrians and automated vehicles by evaluating recent advances in external human-machine interfaces (eHMI), which enable the transmission of state and intent information from the vehicle to the rest of the traffic participants. Recent developments will be explored and studies analyzing their effectiveness based on pedestrian feedback data will be presented and contextualized. As a result, we aim to draw a broad perspective on the current status and recent techniques for eHMI and some guidelines that will encourage future research and development of these systems

    Ramasse-miettes générationnel et incémental gérant les cycles et les gros objets en utilisant des frames délimités

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    Ces dernières années, des recherches ont été menées sur plusieurs techniques reliées à la collection des déchets. Plusieurs découvertes centrales pour le ramassage de miettes par copie ont été réalisées. Cependant, des améliorations sont encore possibles. Dans ce mémoire, nous introduisons des nouvelles techniques et de nouveaux algorithmes pour améliorer le ramassage de miettes. En particulier, nous introduisons une technique utilisant des cadres délimités pour marquer et retracer les pointeurs racines. Cette technique permet un calcul efficace de l'ensemble des racines. Elle réutilise des concepts de deux techniques existantes, card marking et remembered sets, et utilise une configuration bidirectionelle des objets pour améliorer ces concepts en stabilisant le surplus de mémoire utilisée et en réduisant la charge de travail lors du parcours des pointeurs. Nous présentons aussi un algorithme pour marquer récursivement les objets rejoignables sans utiliser de pile (éliminant le gaspillage de mémoire habituel). Nous adaptons cet algorithme pour implémenter un ramasse-miettes copiant en profondeur et améliorer la localité du heap. Nous améliorons l'algorithme de collection des miettes older-first et sa version générationnelle en ajoutant une phase de marquage garantissant la collection de toutes les miettes, incluant les structures cycliques réparties sur plusieurs fenêtres. Finalement, nous introduisons une technique pour gérer les gros objets. Pour tester nos idées, nous avons conçu et implémenté, dans la machine virtuelle libre Java SableVM, un cadre de développement portable et extensible pour la collection des miettes. Dans ce cadre, nous avons implémenté des algorithmes de collection semi-space, older-first et generational. Nos expérimentations montrent que la technique du cadre délimité procure des performances compétitives pour plusieurs benchmarks. Elles montrent aussi que, pour la plupart des benchmarks, notre algorithme de parcours en profondeur améliore la localité et augmente ainsi la performance. Nos mesures de la performance générale montrent que, utilisant nos techniques, un ramasse-miettes peut délivrer une performance compétitive et surpasser celle des ramasses-miettes existants pour plusieurs benchmarks. ______________________________________________________________________________ MOTS-CLÉS DE L’AUTEUR : Ramasse-Miettes, Machine Virtuelle, Java, SableVM

    Vision-Based Autonomous Human Tracking Mobile Robot

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    Tracking moving objects is one of the most important but problematic features of motion analysis and understanding. In order to effectively interact robots with people in close proximity, the systems must first be able to detect, track, and follow people. Following a human with a mobile robot arises in many different service robotic applications. This paper proposes to build an autonomous human tracking mobile robot which can solve the occlusion problem during tracking. The robot can make human tracking efficiently by analysing the information obtained from a camera which is equipped on the top of the robot. The system performs human detection by using Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) algorithms and then uses HSV (Hue Saturation Value) color system for detecting stranger. If the detected human is stranger, robot will make tracking. During the process, the robot needs to track the stranger without missing. So, Kalman filter is used to solve this problem. Kalman filter can estimate the target human when the human is occluded with walls or something. This paper describes the processing results and experimental results of a mobile robot which will track unmarked human efficiently and handle the occlusion using vision sensor and Kalman filter
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