Robust automatic target tracking based on a Bayesian ego-motion compensation framework for airborne FLIR imagery

Automatic target tracking in airborne FLIR imagery is currently a challenge due to the camera ego-motion. This phenomenon distorts the spatio-temporal correlation of the video sequence, which dramatically reduces the tracking performance. Several works address this problem using ego-motion compensation strategies. They use a deterministic approach to compensate the camera motion assuming a specific model of geometric transformation. However, in real sequences a specific geometric transformation can not accurately describe the camera ego-motion for the whole sequence, and as consequence of this, the performance of the tracking stage can significantly decrease, even completely fail. The optimum transformation for each pair of consecutive frames depends on the relative depth of the elements that compose the scene, and their degree of texturization. In this work, a novel Particle Filter framework is proposed to efficiently manage several hypothesis of geometric transformations: Euclidean, affine, and projective. Each type of transformation is used to compute candidate locations of the object in the current frame. Then, each candidate is evaluated by the measurement model of the Particle Filter using the appearance information. This approach is able to adapt to different camera ego-motion conditions, and thus to satisfactorily perform the tracking. The proposed strategy has been tested on the AMCOM FLIR dataset, showing a high efficiency in the tracking of different types of targets in real working conditions

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

Binokulare Eigenbewegungsschätzung für Fahrerassistenzanwendungen

Driving can be dangerous. Humans become inattentive when performing a monotonous task like driving. Also the risk implied while multi-tasking, like using the cellular phone while driving, can break the concentration of the driver and increase the risk of accidents. Others factors like exhaustion, nervousness and excitement affect the performance of the driver and the response time. Consequently, car manufacturers have developed systems in the last decades which assist the driver under various circumstances. These systems are called driver assistance systems. Driver assistance systems are meant to support the task of driving, and the field of action varies from alerting the driver, with acoustical or optical warnings, to taking control of the car, such as keeping the vehicle in the traffic lane until the driver resumes control. For such a purpose, the vehicle is equipped with on-board sensors which allow the perception of the environment and/or the state of the vehicle. Cameras are sensors which extract useful information about the visual appearance of the environment. Additionally, a binocular system allows the extraction of 3D information. One of the main requirements for most camera-based driver assistance systems is the accurate knowledge of the motion of the vehicle. Some sources of information, like velocimeters and GPS, are of common use in vehicles today. Nevertheless, the resolution and accuracy usually achieved with these systems are not enough for many real-time applications. The computation of ego-motion from sequences of stereo images for the implementation of driving intelligent systems, like autonomous navigation or collision avoidance, constitutes the core of this thesis. This dissertation proposes a framework for the simultaneous computation of the 6 degrees of freedom of ego-motion (rotation and translation in 3D Euclidean space), the estimation of the scene structure and the detection and estimation of independently moving objects. The input is exclusively provided by a binocular system and the framework does not call for any data acquisition strategy, i.e. the stereo images are just processed as they are provided. Stereo allows one to establish correspondences between left and right images, estimating 3D points of the environment via triangulation. Likewise, feature tracking establishes correspondences between the images acquired at different time instances. When both are used together for a large number of points, the result is a set of clouds of 3D points with point-to-point correspondences between clouds. The apparent motion of the 3D points between consecutive frames is caused by a variety of reasons. The most dominant motion for most of the points in the clouds is caused by the ego-motion of the vehicle; as the vehicle moves and images are acquired, the relative position of the world points with respect to the vehicle changes. Motion is also caused by objects moving in the environment. They move independently of the vehicle motion, so the observed motion for these points is the sum of the ego-vehicle motion and the independent motion of the object. A third reason, and of paramount importance in vision applications, is caused by correspondence problems, i.e. the incorrect spatial or temporal assignment of the point-to-point correspondence. Furthermore, all the points in the clouds are actually noisy measurements of the real unknown 3D points of the environment. Solving ego-motion and scene structure from the clouds of points requires some previous analysis of the noise involved in the imaging process, and how it propagates as the data is processed. Therefore, this dissertation analyzes the noise properties of the 3D points obtained through stereo triangulation. This leads to the detection of a bias in the estimation of 3D position, which is corrected with a reformulation of the projection equation. Ego-motion is obtained by finding the rotation and translation between the two clouds of points. This problem is known as absolute orientation, and many solutions based on least squares have been proposed in the literature. This thesis reviews the available closed form solutions to the problem. The proposed framework is divided in three main blocks: 1) stereo and feature tracking computation, 2) ego-motion estimation and 3) estimation of 3D point position and 3D velocity. The first block solves the correspondence problem providing the clouds of points as output. No special implementation of this block is required in this thesis. The ego-motion block computes the motion of the cameras by finding the absolute orientation between the clouds of static points in the environment. Since the cloud of points might contain independently moving objects and outliers generated by false correspondences, the direct computation of the least squares might lead to an erroneous solution. The first contribution of this thesis is an effective rejection rule that detects outliers based on the distance between predicted and measured quantities, and reduces the effects of noisy measurement by assigning appropriate weights to the data. This method is called Smoothness Motion Constraint (SMC). The ego-motion of the camera between two frames is obtained finding the absolute orientation between consecutive clouds of weighted 3D points. The complete ego-motion since initialization is achieved concatenating the individual motion estimates. This leads to a super-linear propagation of the error, since noise is integrated. A second contribution of this dissertation is a predictor/corrector iterative method, which integrates the clouds of 3D points of multiple time instances for the computation of ego-motion. The presented method considerably reduces the accumulation of errors in the estimated ego-position of the camera. Another contribution of this dissertation is a method which recursively estimates the 3D world position of a point and its velocity; by fusing stereo, feature tracking and the estimated ego-motion in a Kalman Filter system. An improved estimation of point position is obtained this way, which is used in the subsequent system cycle resulting in an improved computation of ego-motion. The general contribution of this dissertation is a single framework for the real time computation of scene structure, independently moving objects and ego-motion for automotive applications.Autofahren kann gefährlich sein. Die Fahrleistung wird durch die physischen und psychischen Grenzen des Fahrers und durch externe Faktoren wie das Wetter beeinflusst. Fahrerassistenzsysteme erhöhen den Fahrkomfort und unterstützen den Fahrer, um die Anzahl an Unfällen zu verringern. Fahrerassistenzsysteme unterstützen den Fahrer durch Warnungen mit optischen oder akustischen Signalen bis hin zur Übernahme der Kontrolle über das Auto durch das System. Eine der Hauptvoraussetzungen für die meisten Fahrerassistenzsysteme ist die akkurate Kenntnis der Bewegung des eigenen Fahrzeugs. Heutzutage verfügt man über verschiedene Sensoren, um die Bewegung des Fahrzeugs zu messen, wie zum Beispiel GPS und Tachometer. Doch Auflösung und Genauigkeit dieser Systeme sind nicht ausreichend für viele Echtzeitanwendungen. Die Berechnung der Eigenbewegung aus Stereobildsequenzen für Fahrerassistenzsysteme, z.B. zur autonomen Navigation oder Kollisionsvermeidung, bildet den Kern dieser Arbeit. Diese Dissertation präsentiert ein System zur Echtzeitbewertung einer Szene, inklusive Detektion und Bewertung von unabhängig bewegten Objekten sowie der akkuraten Schätzung der sechs Freiheitsgrade der Eigenbewegung. Diese grundlegenden Bestandteile sind erforderlich, um viele intelligente Automobilanwendungen zu entwickeln, die den Fahrer in unterschiedlichen Verkehrssituationen unterstützen. Das System arbeitet ausschließlich mit einer Stereokameraplattform als Sensor. Um die Eigenbewegung und die Szenenstruktur zu berechnen wird eine Analyse des Rauschens und der Fehlerfortpflanzung im Bildaufbereitungsprozess benötigt. Deshalb werden in dieser Dissertation die Rauscheigenschaften der durch Stereotriangulation erhaltenen 3D-Punkte analysiert. Dies führt zu der Entdeckung eines systematischen Fehlers in der Schätzung der 3D-Position, der sich mit einer Neuformulierung der Projektionsgleichung korrigieren lässt. Die Simulationsergebnisse zeigen, dass eine bedeutende Verringerung des Fehlers in der geschätzten 3D-Punktposition möglich ist. Die Eigenbewegungsschätzung wird gewonnen, indem die Rotation und Translation zwischen Punktwolken geschätzt wird. Dieses Problem ist als „absolute Orientierung” bekannt und viele Lösungen auf Basis der Methode der kleinsten Quadrate sind in der Literatur vorgeschlagen worden. Diese Arbeit rezensiert die verfügbaren geschlossenen Lösungen zu dem Problem. Das vorgestellte System gliedert sich in drei wesentliche Bausteine: 1. Registrierung von Bildmerkmalen, 2. Eigenbewegungsschätzung und 3. iterative Schätzung von 3D-Position und 3D-Geschwindigkeit von Weltpunkten. Der erster Block erhält eine Folge rektifizierter Bilder als Eingabe und liefert daraus eine Liste von verfolgten Bildmerkmalen mit ihrer entsprechenden 3D-Position. Der Block „Eigenbewegungsschätzung” besteht aus vier Hauptschritten in einer Schleife: 1. Bewegungsvorhersage, 2. Anwendung der Glattheitsbedingung für die Bewegung (GBB), 3. absolute Orientierungsberechnung und 4. Bewegungsintegration. Die in dieser Dissertation vorgeschlagene GBB ist eine mächtige Bedingung für die Ablehnung von Ausreißern und für die Zuordnung von Gewichten zu den gemessenen 3D-Punkten. Simulationen werden mit gaußschem und slashschem Rauschen ausgeführt. Die Ergebnisse zeigen die Überlegenheit der GBB-Version über die Standardgewichtungsmethoden. Die Stabilität der Ergebnisse hinsichtlich Ausreißern wurde analysiert mit dem Resultat, dass der „break down point” größer als 50% ist. Wenn die vier Schritte iterativ ausgeführt, werden wird ein Prädiktor-Korrektor-Verfahren gewonnen.Wir nennen diese Schätzung Multi-frameschätzung im Gegensatz zur Zweiframeschätzung, die nur die aktuellen und vorherigen Bildpaare für die Berechnung der Eigenbewegung betrachtet. Die erste Iteration wird zwischen der aktuellen und vorherigen Wolke von Punkten durchgeführt. Jede weitere Iteration integriert eine zusätzliche Punktwolke eines vorherigen Zeitpunkts. Diese Methode reduziert die Fehlerakkumulation bei der Integration von mehreren Schätzungen in einer einzigen globalen Schätzung. Simulationsergebnisse zeigen, dass obwohl der Fehler noch superlinear im Laufe der Zeit zunimmt, die Größe des Fehlers um mehrere Größenordnungen reduziert wird. Der dritte Block besteht aus der iterativen Schätzung von 3D-Position und 3D-Geschwindigkeit von Weltpunkten. Hier wird eine Methode basierend auf einem Kalman Filter verwendet, das Stereo, Featuretracking und Eigenbewegungsdaten fusioniert. Messungen der Position eines Weltpunkts werden durch das Stereokamerasystem gewonnen. Die Differenzierung der Position des geschätzten Punkts erlaubt die zusätzliche Schätzung seiner Geschwindigkeit. Die Messungen werden durch das Messmodell gewonnen, das Stereo- und Bewegungsdaten fusioniert. Simulationsergebnisse validieren das Modell. Die Verringerung der Positionsunsicherheit im Laufe der Zeit wird mit einer Monte-Carlo Simulation erzielt. Experimentelle Ergebnisse werden mit langen Sequenzen von Bildern erzielt. Zusätzliche Tests, einschließlich einer 3D-Rekonstruktion einer Waldszene und der Berechnung der freien Kamerabewegung in einem Indoor-Szenario, wurden durchgeführt. Die Methode zeigt gute Ergebnisse in allen Fällen. Der Algorithmus liefert zudem akzeptable Ergebnisse bei der Schätzung der Lage kleiner Objekte, wie Köpfe und Beine von realen Crash-Test-Dummies

What can be done with an embedded stereo-rig in urban environments?

International audienceThe development of the Autonomous Guided Vehicles (AGVs) with urban applications are now possible due to the recent solutions (DARPA Grand Challenge) developed to solve the Simultaneous Localization And Mapping (SLAM) problem: perception, path planning and control. For the last decade, the introduction of GPS systems and vision have been allowed the transposition of SLAM methods dedicated to indoor environments to outdoor ones. When the GPS data are unavailable, the current position of the mobile robot can be estimated by the fusion of data from odometer and/or Inertial Navigation System (INS). We detail in this article what can be done with an uncalibrated stereo-rig, when it is embedded in a vehicle which is going through urban roads. The methodology is based on features extracted on planes: we mainly assume the road at the foreground as the plane common to all the urban scenes but other planes like vertical frontages of buildings can be used if the features extracted on the road are not enough relevant. The relative motions of the coplanar features tracked with both cameras allow us to stimate the vehicle ego-motion with a high precision. Futhermore, the features which don't check the relative motion of the considered plane can be assumed as obstacles

04251 -- Imaging Beyond the Pinhole Camera

From 13.06.04 to 18.06.04, the Dagstuhl Seminar 04251 ``Imaging Beyond the Pin-hole Camera. 12th Seminar on Theoretical Foundations of Computer Vision\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Detection of Obstacles in Monocular Image Sequences

The ability to detect and locate runways/taxiways and obstacles in images captured using on-board sensors is an essential first step in the automation of low-altitude flight, landing, takeoff, and taxiing phase of aircraft navigation. Automation of these functions under different weather and lighting situations, can be facilitated by using sensors of different modalities. An aircraft-based Synthetic Vision System (SVS), with sensors of different modalities mounted on-board, complements the current ground-based systems in functions such as detection and prevention of potential runway collisions, airport surface navigation, and landing and takeoff in all weather conditions. In this report, we address the problem of detection of objects in monocular image sequences obtained from two types of sensors, a Passive Millimeter Wave (PMMW) sensor and a video camera mounted on-board a landing aircraft. Since the sensors differ in their spatial resolution, and the quality of the images obtained using these sensors is not the same, different approaches are used for detecting obstacles depending on the sensor type. These approaches are described separately in two parts of this report. The goal of the first part of the report is to develop a method for detecting runways/taxiways and objects on the runway in a sequence of images obtained from a moving PMMW sensor. Since the sensor resolution is low and the image quality is very poor, we propose a model-based approach for detecting runways/taxiways. We use the approximate runway model and the position information of the camera provided by the Global Positioning System (GPS) to define regions of interest in the image plane to search for the image features corresponding to the runway markers. Once the runway region is identified, we use histogram-based thresholding to detect obstacles on the runway and regions outside the runway. This algorithm is tested using image sequences simulated from a single real PMMW image

ROBUST BACKGROUND SUBTRACTION FOR MOVING CAMERAS AND THEIR APPLICATIONS IN EGO-VISION SYSTEMS

Background subtraction is the algorithmic process that segments out the region of interest often known as foreground from the background. Extensive literature and numerous algorithms exist in this domain, but most research have focused on videos captured by static cameras. The proliferation of portable platforms equipped with cameras has resulted in a large amount of video data being generated from moving cameras. This motivates the need for foundational algorithms for foreground/background segmentation in videos from moving cameras. In this dissertation, I propose three new types of background subtraction algorithms for moving cameras based on appearance, motion, and a combination of them. Comprehensive evaluation of the proposed approaches on publicly available test sequences show superiority of our system over state-of-the-art algorithms. The first method is an appearance-based global modeling of foreground and background. Features are extracted by sliding a fixed size window over the entire image without any spatial constraint to accommodate arbitrary camera movements. Supervised learning method is then used to build foreground and background models. This method is suitable for limited scene scenarios such as Pan-Tilt-Zoom surveillance cameras. The second method relies on motion. It comprises of an innovative background motion approximation mechanism followed by spatial regulation through a Mega-Pixel denoising process. This work does not need to maintain any costly appearance models and is therefore appropriate for resource constraint ego-vision systems. The proposed segmentation combined with skin cues is validated by a novel application on authenticating hand-gestured signature captured by wearable cameras. The third method combines both motion and appearance. Foreground probabilities are jointly estimated by motion and appearance. After the mega-pixel denoising process, the probability estimates and gradient image are combined by Graph-Cut to produce the segmentation mask. This method is universal as it can handle all types of moving cameras

A cognitive ego-vision system for interactive assistance

With increasing computational power and decreasing size, computers nowadays are already wearable and mobile. They become attendant of peoples' everyday life. Personal digital assistants and mobile phones equipped with adequate software gain a lot of interest in public, although the functionality they provide in terms of assistance is little more than a mobile databases for appointments, addresses, to-do lists and photos. Compared to the assistance a human can provide, such systems are hardly to call real assistants. The motivation to construct more human-like assistance systems that develop a certain level of cognitive capabilities leads to the exploration of two central paradigms in this work. The first paradigm is termed cognitive vision systems. Such systems take human cognition as a design principle of underlying concepts and develop learning and adaptation capabilities to be more flexible in their application. They are embodied, active, and situated. Second, the ego-vision paradigm is introduced as a very tight interaction scheme between a user and a computer system that especially eases close collaboration and assistance between these two. Ego-vision systems (EVS) take a user's (visual) perspective and integrate the human in the system's processing loop by means of a shared perception and augmented reality. EVSs adopt techniques of cognitive vision to identify objects, interpret actions, and understand the user's visual perception. And they articulate their knowledge and interpretation by means of augmentations of the user's own view. These two paradigms are studied as rather general concepts, but always with the goal in mind to realize more flexible assistance systems that closely collaborate with its users. This work provides three major contributions. First, a definition and explanation of ego-vision as a novel paradigm is given. Benefits and challenges of this paradigm are discussed as well. Second, a configuration of different approaches that permit an ego-vision system to perceive its environment and its user is presented in terms of object and action recognition, head gesture recognition, and mosaicing. These account for the specific challenges identified for ego-vision systems, whose perception capabilities are based on wearable sensors only. Finally, a visual active memory (VAM) is introduced as a flexible conceptual architecture for cognitive vision systems in general, and for assistance systems in particular. It adopts principles of human cognition to develop a representation for information stored in this memory. So-called memory processes continuously analyze, modify, and extend the content of this VAM. The functionality of the integrated system emerges from their coordinated interplay of these memory processes. An integrated assistance system applying the approaches and concepts outlined before is implemented on the basis of the visual active memory. The system architecture is discussed and some exemplary processing paths in this system are presented and discussed. It assists users in object manipulation tasks and has reached a maturity level that allows to conduct user studies. Quantitative results of different integrated memory processes are as well presented as an assessment of the interactive system by means of these user studies