The Visual Social Distancing Problem

One of the main and most effective measures to contain the recent viral outbreak is the maintenance of the so-called Social Distancing (SD). To comply with this constraint, workplaces, public institutions, transports and schools will likely adopt restrictions over the minimum inter-personal distance between people. Given this actual scenario, it is crucial to massively measure the compliance to such physical constraint in our life, in order to figure out the reasons of the possible breaks of such distance limitations, and understand if this implies a possible threat given the scene context. All of this, complying with privacy policies and making the measurement acceptable. To this end, we introduce the Visual Social Distancing (VSD) problem, defined as the automatic estimation of the inter-personal distance from an image, and the characterization of the related people aggregations. VSD is pivotal for a non-invasive analysis to whether people comply with the SD restriction, and to provide statistics about the level of safety of specific areas whenever this constraint is violated. We then discuss how VSD relates with previous literature in Social Signal Processing and indicate which existing Computer Vision methods can be used to manage such problem. We conclude with future challenges related to the effectiveness of VSD systems, ethical implications and future application scenarios.Comment: 9 pages, 5 figures. All the authors equally contributed to this manuscript and they are listed by alphabetical order. Under submissio

Ground plane detection using Kinect Sensor

Görüntü işleme tabanlı mobil robotların başarılı navigasyonu için zemin düzlemi algılama esastır. Bu bildiride Kinect derinlik sensöründen elde edilen derinlik bilgisine dayalı yeni ve gürbüz bir zemin düzlemi algılama algoritması önerilmektedir. Literatürdeki benzer yöntemlerin aksine, zemin düzleminin sahnedeki en büyük alan olduğunu varsayılmamaktadır. Yöntemimiz sensörün yeri görüş açısının sabit olduğu veya değişken olabileceği iki farklı durum için iki değişik algoritma halinde sunulmaktadır. Yaptığımız deneylerde her iki durum için önerdiğimiz algoritmaların oldukça başarılı olduğu gösterilmektedir.Ground plane detection is essential for successful navigation of vision based mobile robots. We introduce a novel and robust ground plane detection algorithm using depth information acquired by a Kinect sensor. Unlike similar methods from the literature, we do not assume that the ground plane covers the largest area in the scene. Furthermore our algorithm handles two different conditions: fixed and changing view angle of the sensor. We show that the algorithm is robust if the view angle is fixed whereas an additional procedure handles different view angles satisfactorily.Publisher's VersionAuthor Post Prin

Detection of dominant planar surfaces in disparity images based on random sampling

U ovom članku ispituje se praktična primjenjivost RANSAC-pristupa za detekciju ravnih površina na slikama dispariteta dobivenim pomoću stereo vizije. Težište istraživanja je primjena u interijerima, gdje je velik dio dominantnih površina jednolično obojen, što predstavlja poseban problem za stereo viziju. Ispitano je nekoliko jednostavnih modifikacija osnovnog RANSAC-algoritma s ciljem utvrđivanja koliko oni mogu poboljšati njegovu učinkovitost. Predložene su dvije jednostavne mjere točnosti rekonstrukcija ravnih površina. Provedeno je eksperimentalno istraživanje na slikama snimljenim sustavom stereo vizije montiranom na mobilnog robota koji se kretao hodnicima fakulteta.In this paper, the applicability of RANSAC-approach to planar surface detection in disparity images obtained by stereo vision is investigated. This study is specially focused on application in indoor environments, where many of the dominant surfaces are uniformly colored, which poses additional difficulties to stereo vision. Several simple modifications to the basic RANSAC-algorithm are examined and improvements achieved by these modifications are evaluated. Two simple performance measures for evaluating the accuracy of planar surface detection are proposed. An experimental study is performed using images acquired by a stereo vision system mounted on a mobile robot moving in an indoor environment

Statistical modelling of algorithms for signal processing in systems based on environment perception

One cornerstone for realising automated driving systems is an appropriate handling of uncertainties in the environment perception and situation interpretation. Uncertainties arise due to noisy sensor measurements or the unknown future evolution of a traffic situation. This work contributes to the understanding of these uncertainties by modelling and propagating them with parametric probability distributions

Automated construction technologies : analyses and future development strategies

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Architecture, 2005.Page 140 blank.Includes bibliographical references (p. 130-132).Substandard productivity and the lack of skilled workers in the construction industry have led major corporations all over the world aiming to produce various types of automated construction technologies. During the process, novel ideologies of design and construction techniques have been realized and the push for these applications has never been greater. This thesis will look to answer the question of benefit and effectiveness of automated construction technology. It will focus on three basic concepts: 1) Analyzing existing automated construction technology; 2) understanding of automated machine design and components; 3) proposal of a multi-purpose pick-and-place machine for the automated construction process. In the end, the intention will be to promote a design intensive approach to automated construction technology in order to advance the conventional methodologies of design and construction.by Han Hoang.S.M

Probabilistic Models for 3D Urban Scene Understanding from Movable Platforms

This work is a contribution to understanding multi-object traffic scenes from video sequences. All data is provided by a camera system which is mounted on top of the autonomous driving platform AnnieWAY. The proposed probabilistic generative model reasons jointly about the 3D scene layout as well as the 3D location and orientation of objects in the scene. In particular, the scene topology, geometry as well as traffic activities are inferred from short video sequences

Dichte Objektsegmentierung in Stereobildfolgen

Die in dieser Arbeit vorgestellte Szenensegmentierung zerlegt eine Stereobildsequenz in eine Menge von Bildbereichen, die vom Menschen eindeutig als unabhängig bewegte Verkehrsobjekte interpretierbar sind. Eine Besonderheit des Verfahrens ist die Tatsache, dass die einzelnen Teilaufgaben der dreidimensionalen Rekonstruktion, Bewegungsschätzung und Segmentierung dabei in einem gemeinsamen Modell beschrieben und in verzahnter Reihenfolge gelöst werden

Building and sharing the cognition model of the environment with collaborative service robots

This research presents a shared cognition concept for building a cognition model collaboratively by robot and human. The proposed concept is based on an object-oriented approach that abstracts robot's perception, cognition, and knowledge to separate but interconnected elements. The shared cognition concept allows the human and robot share information related to objects in the environment for effective task execution and information exchange. The concept enables building a model using sensors, as well as inputting data from the user. User can utilize also robot's sensor data for inputting information. This utilizes the robot's perception of the environment in conjunction with human's cognitive understanding. The proposed concept can be used for exchanging information on the objects, their classes, identities, appearances, locations, structures, and states. Representation of perceived entities is done through observed objects, robot's cognitive understanding of the objects is represented by real objects, and the knowledge of objects' classes by meta-objects. Observed objects are created with segmentation which can be autonomous or assisted by human. Real objects are used to transfer the actual object information between human and robot. Recognition of objects is done by matching real and observed objects. Uncertainties of recognition and localization are handled by probabilistic approach. The model enables learning from perceived information allowing the model to improve its performance as more data is gathered. The cognition model can be used for collaborative task execution. Several aspects of the model are validated with two different platforms, WorkPartner service robot and Avant machine based semi-autonomous robot. Based on the tests, the proposed model is an effective mechanism for collaboratively exchanging spatial information between a robot and a human