A 3D descriptor to detect task-oriented grasping points in clothing

© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Manipulating textile objects with a robot is a challenging task, especially because the garment perception is difficult due to the endless configurations it can adopt, coupled with a large variety of colors and designs. Most current approaches follow a multiple re-grasp strategy, in which clothes are sequentially grasped from different points until one of them yields a recognizable configuration. In this work we propose a method that combines 3D and appearance information to directly select a suitable grasping point for the task at hand, which in our case consists of hanging a shirt or a polo shirt from a hook. Our method follows a coarse-to-fine approach in which, first, the collar of the garment is detected and, next, a grasping point on the lapel is chosen using a novel 3D descriptor. In contrast to current 3D descriptors, ours can run in real time, even when it needs to be densely computed over the input image. Our central idea is to take advantage of the structured nature of range images that most depth sensors provide and, by exploiting integral imaging, achieve speed-ups of two orders of magnitude with respect to competing approaches, while maintaining performance. This makes it especially adequate for robotic applications as we thoroughly demonstrate in the experimental section.Peer ReviewedPostprint (author's final draft

A framework for cardio-pulmonary resuscitation (CPR) scene retrieval from medical simulation videos based on object and activity detection.

In this thesis, we propose a framework to detect and retrieve CPR activity scenes from medical simulation videos. Medical simulation is a modern training method for medical students, where an emergency patient condition is simulated on human-like mannequins and the students act upon. These simulation sessions are recorded by the physician, for later debriefing. With the increasing number of simulation videos, automatic detection and retrieval of specific scenes became necessary. The proposed framework for CPR scene retrieval, would eliminate the conventional approach of using shot detection and frame segmentation techniques. Firstly, our work explores the application of Histogram of Oriented Gradients in three dimensions (HOG3D) to retrieve the scenes containing CPR activity. Secondly, we investigate the use of Local Binary Patterns in Three Orthogonal Planes (LBPTOP), which is the three dimensional extension of the popular Local Binary Patterns. This technique is a robust feature that can detect specific activities from scenes containing multiple actors and activities. Thirdly, we propose an improvement to the above mentioned methods by a combination of HOG3D and LBP-TOP. We use decision level fusion techniques to combine the features. We prove experimentally that the proposed techniques and their combination out-perform the existing system for CPR scene retrieval. Finally, we devise a method to detect and retrieve the scenes containing the breathing bag activity, from the medical simulation videos. The proposed framework is tested and validated using eight medical simulation videos and the results are presented

Three-dimensional Laser-based Classification in Outdoor Environments

Robotics research strives for deploying autonomous systems in populated environments, such as inner city traffic. Autonomous cars need a reliable collision avoidance, but also an object recognition to distinguish different classes of traffic participants. For both tasks, fast three-dimensional laser range sensors generating multiple accurate laser range scans per second, each consisting of a vast number of laser points, are often employed. In this thesis, we investigate and develop classification algorithms that allow us to automatically assign semantic labels to laser scans. We mainly face two challenges: (1) we have to ensure consistent and correct classification results and (2) we must efficiently process a vast number of laser points per scan. In consideration of these challenges, we cover both stages of classification -- the feature extraction from laser range scans and the classification model that maps from the features to semantic labels. As for the feature extraction, we contribute by thoroughly evaluating important state-of-the-art histogram descriptors. We investigate critical parameters of the descriptors and experimentally show for the first time that the classification performance can be significantly improved using a large support radius and a global reference frame. As for learning the classification model, we contribute with new algorithms that improve the classification efficiency and accuracy. Our first approach aims at deriving a consistent point-wise interpretation of the whole laser range scan. By combining efficient similarity-preserving hashing and multiple linear classifiers, we considerably improve the consistency of label assignments, requiring only minimal computational overhead compared to a single linear classifier. In the last part of the thesis, we aim at classifying objects represented by segments. We propose a novel hierarchical segmentation approach comprising multiple stages and a novel mixture classification model of multiple bag-of-words vocabularies. We demonstrate superior performance of both approaches compared to their single component counterparts using challenging real world datasets.Ziel des Forschungsbereichs Robotik ist der Einsatz autonomer Systeme in natürlichen Umgebungen, wie zum Beispiel innerstädtischem Verkehr. Autonome Fahrzeuge benötigen einerseits eine zuverlässige Kollisionsvermeidung und andererseits auch eine Objekterkennung zur Unterscheidung verschiedener Klassen von Verkehrsteilnehmern. Verwendung finden vorallem drei-dimensionale Laserentfernungssensoren, die mehrere präzise Laserentfernungsscans pro Sekunde erzeugen und jeder Scan besteht hierbei aus einer hohen Anzahl an Laserpunkten. In dieser Dissertation widmen wir uns der Untersuchung und Entwicklung neuartiger Klassifikationsverfahren zur automatischen Zuweisung von semantischen Objektklassen zu Laserpunkten. Hierbei begegnen wir hauptsächlich zwei Herausforderungen: (1) wir möchten konsistente und korrekte Klassifikationsergebnisse erreichen und (2) die immense Menge an Laserdaten effizient verarbeiten. Unter Berücksichtigung dieser Herausforderungen untersuchen wir beide Verarbeitungsschritte eines Klassifikationsverfahrens -- die Merkmalsextraktion unter Nutzung von Laserdaten und das eigentliche Klassifikationsmodell, welches die Merkmale auf semantische Objektklassen abbildet. Bezüglich der Merkmalsextraktion leisten wir ein Beitrag durch eine ausführliche Evaluation wichtiger Histogrammdeskriptoren. Wir untersuchen kritische Deskriptorparameter und zeigen zum ersten Mal, dass die Klassifikationsgüte unter Nutzung von großen Merkmalsradien und eines globalen Referenzrahmens signifikant gesteigert wird. Bezüglich des Lernens des Klassifikationsmodells, leisten wir Beiträge durch neue Algorithmen, welche die Effizienz und Genauigkeit der Klassifikation verbessern. In unserem ersten Ansatz möchten wir eine konsistente punktweise Interpretation des gesamten Laserscans erreichen. Zu diesem Zweck kombinieren wir eine ähnlichkeitserhaltende Hashfunktion und mehrere lineare Klassifikatoren und erreichen hierdurch eine erhebliche Verbesserung der Konsistenz der Klassenzuweisung bei minimalen zusätzlichen Aufwand im Vergleich zu einem einzelnen linearen Klassifikator. Im letzten Teil der Dissertation möchten wir Objekte, die als Segmente repräsentiert sind, klassifizieren. Wir stellen eine neuartiges hierarchisches Segmentierungsverfahren und ein neuartiges Klassifikationsmodell auf Basis einer Mixtur mehrerer bag-of-words Vokabulare vor. Wir demonstrieren unter Nutzung von praxisrelevanten Datensätzen, dass beide Ansätze im Vergleich zu ihren Entsprechungen aus einer einzelnen Komponente zu erheblichen Verbesserungen führen

Review of Person Re-identification Techniques

Person re-identification across different surveillance cameras with disjoint fields of view has become one of the most interesting and challenging subjects in the area of intelligent video surveillance. Although several methods have been developed and proposed, certain limitations and unresolved issues remain. In all of the existing re-identification approaches, feature vectors are extracted from segmented still images or video frames. Different similarity or dissimilarity measures have been applied to these vectors. Some methods have used simple constant metrics, whereas others have utilised models to obtain optimised metrics. Some have created models based on local colour or texture information, and others have built models based on the gait of people. In general, the main objective of all these approaches is to achieve a higher-accuracy rate and lowercomputational costs. This study summarises several developments in recent literature and discusses the various available methods used in person re-identification. Specifically, their advantages and disadvantages are mentioned and compared.Comment: Published 201