Automatische Registrierung adaptiver Modelle zur Typerkennung technischer Objekte [online]

Anwendungen der Bildanalyse werden in zunehmendem Maße unter Verwendung dreidimensionaler Modelle realisiert und fusionieren auf diese Weise Methoden der Computergrafik und der Bildauswertung. Mit dem Ziel der automatischen Erfassung dynamischer Szenenaktivitäten ist in den letzten Jahren ein vermehrter Einsatz adaptiver Modelle zu beobachten. In der vorliegenden Arbeit wird ein neu entwickeltes Verfahren zur automatischen Konstruktion adaptiver Modelle für technische Objekte vorgestellt. Ferner werden Module zur automatischen Anpassung dieser adaptiven Modelle an Grauwertbilder beschrieben, die durch Synthese-Analyse-Iterationen die Brücke zur Bildanalyse schlagen. Die zentrale Stärke der vorgestellten Komponenten liegt darin, dass sie aus Einzelbildern dreidimensionale Rekonstruktionen für unbekannte Objektvarianten liefern. Wie experimentell gezeigt wird, sind diese Rekonstruktionen geometrisch genauer als handelsübliche Modelle. Die Leistungsfähigkeit der entwickelten Verfahren wird am Beispiel der Flugzeugtypisierung gezeigt. Darüber hinaus wird die Anwendbarkeit der Module zur Lageschätzung demonstriert

Automatische Registrierung adaptiver Modelle zur Typerkennung technischer Objekte [online]

Anwendungen der Bildanalyse werden in zunehmendem Maße unter Verwendung dreidimensionaler Modelle realisiert und fusionieren auf diese Weise Methoden der Computergrafik und der Bildauswertung. Mit dem Ziel der automatischen Erfassung dynamischer Szenenaktivitäten ist in den letzten Jahren ein vermehrter Einsatz adaptiver Modelle zu beobachten. In der vorliegenden Arbeit wird ein neu entwickeltes Verfahren zur automatischen Konstruktion adaptiver Modelle für technische Objekte vorgestellt. Ferner werden Module zur automatischen Anpassung dieser adaptiven Modelle an Grauwertbilder beschrieben, die durch Synthese-Analyse-Iterationen die Brücke zur Bildanalyse schlagen. Die zentrale Stärke der vorgestellten Komponenten liegt darin, dass sie aus Einzelbildern dreidimensionale Rekonstruktionen für unbekannte Objektvarianten liefern. Wie experimentell gezeigt wird, sind diese Rekonstruktionen geometrisch genauer als handelsübliche Modelle. Die Leistungsfähigkeit der entwickelten Verfahren wird am Beispiel der Flugzeugtypisierung gezeigt. Darüber hinaus wird die Anwendbarkeit der Module zur Lageschätzung demonstriert

Joint Probabilistic People Detection in Overlapping Depth Images

Privacy-preserving high-quality people detection is a vital computer vision task for various indoor scenarios, e.g. people counting, customer behavior analysis, ambient assisted living or smart homes. In this work a novel approach for people detection in multiple overlapping depth images is proposed. We present a probabilistic framework utilizing a generative scene model to jointly exploit the multi-view image evidence, allowing us to detect people from arbitrary viewpoints. Our approach makes use of mean-field variational inference to not only estimate the maximum a posteriori (MAP) state but to also approximate the posterior probability distribution of people present in the scene. Evaluation shows state-of-the-art results on a novel data set for indoor people detection and tracking in depth images from the top-view with high perspective distortions. Furthermore it can be demonstrated that our approach (compared to the the mono-view setup) successfully exploits the multi-view image evidence and robustly converges in only a few iterations

Temporal Smoothing for Joint Probabilistic People Detection in a Depth Sensor Network

Wide-area indoor people detection in a network of depth sensors is the basis for many applications, e.g. people counting or customer behavior analysis. Existing probabilistic methods use approximative stochastic inference to estimate the marginal probability distribution of people present in the scene for a single time step. In this work we investigate how the temporal context, given by a time series of multi-view depth observations, can be exploited to regularize a mean-field variational inference optimization process. We present a probabilistic grid based dynamic model and deduce the corresponding mean-field update regulations to effectively approximate the joint probability distribution of people present in the scene across space and time. Our experiments show that the proposed temporal regularization leads to a more robust estimation of the desired probability distribution and increases the detection performance

People Detection in a Depth Sensor Network via Multi-View CNNs trained on Synthetic Data

In this work an approach for wide-area indoor people detection with a network of depth sensors is presented. We propose an end-to-end multi-view deep learning architecture which takes three foreground segmented overlapping depth images as input and predicts the marginal probability distribution of people present in the scene. In contrast to classical data-driven approaches our method does not make use of any real image data for training but uses a randomized generative scene model to generate synthetic depth images which are used to train our proposed deep learning architecture. The evaluation shows promising results on a publicly available data set

Automatische Registrierung adaptiver Modelle zur Typerkennung technischer Objekte [online]

Anwendungen der Bildanalyse werden in zunehmendem Maße unter Verwendung dreidimensionaler Modelle realisiert und fusionieren auf diese Weise Methoden der Computergrafik und der Bildauswertung. Mit dem Ziel der automatischen Erfassung dynamischer Szenenaktivitäten ist in den letzten Jahren ein vermehrter Einsatz adaptiver Modelle zu beobachten.In der vorliegenden Arbeit wird ein neu entwickeltes Verfahren zur automatischen Konstruktion adaptiver Modelle für technische Objekte vorgestellt. Ferner werden Module zur automatischen Anpassung dieser adaptiven Modelle an Grauwertbilder beschrieben, die durch Synthese-Analyse-Iterationen die Brücke zur Bildanalyse schlagen. Die zentrale Stärke der vorgestellten Komponenten liegt darin, dass sie aus Einzelbildern dreidimensionale Rekonstruktionen für unbekannte Objektvarianten liefern. Wie experimentell gezeigt wird, sind diese Rekonstruktionen geometrisch genauer als handelsübliche Modelle. Die Leistungsfähigkeit der entwickelten Verfahren wird am Beispiel der Flugzeugtypisierung gezeigt. Darüber hinaus wird die Anwendbarkeit der Module zur Lageschätzung demonstriert

3D Pose and Shape Estimation with Deformable Models in Lifelike Scenes

Abstract — In real world scenes a large variety of objects can occur, that have to be recognized and localized by humanoids. In this paper we propose a new approach to initialize threedimensional pose and shape parameters for a large variety of objects, which is applicable to single images. In order to feed the recognition system with a priori knowledge, three-dimensional models are used and for the purpose of coping shape variations, deformable variants of these models are built. Thereby, threedimensional object descriptions, which incorporate shape parameters, are provided to the recognition system. By discretizing relevant shape and pose parameters, synthetic model views are created. A new method for the selection of the best fitting model view is proposed, which is realized, by applying a chain of filters on large sets of relevant model views. The pose parameters, that are associated with the selected model view, are enhanced in precision by means of a model-based parameter optimization technique. Overall, the approach allows to cope with strongly variable object shapes by combining the benefits of appearancebased and deformable model-based approaches. We present experimental results, proving the high variability of the proposed method and its robustness against partial occlusions. Furthermore, the method was applied to a real world scene, where the estimated pose parameters and the three-dimensional model were provided to a tracking application. I

Tackling Key Challenges of AI Development – Insights from an Industry-Academia Collaboration

Harnessing the overall benefits of the latest advancements in artificial intelligence (AI) requires the extensive collaboration of academia and industry. These collaborations promote innovation and growth while enforcing the practical usefulness of newer technologies in real life. The purpose of this article is to outline the challenges faced during cross-collaboration between academia and industry. These challenges are also inspected with the help of an ongoing project titled “Quality Assurance of Machine Learning Applications” (Q-AMeLiA), in which three universities cooperate with five industry partners to make the product risk of AI-based products visible. Further, we discuss the hurdles and the key challenges in machine learning (ML) technology transformation from academia to industry based on robustness, simplicity, and safety. These challenges are an outcome of the lack of common standards, metrics, and missing regulatory considerations when state-of-the-art (SOTA) technology is developed in academia. The use of biased datasets involves ethical concerns that might lead to unfair outcomes when the ML model is deployed in production. The advancement of AI in small and medium sized enterprises (SMEs) requires more in terms of common tandardization of concepts rather than algorithm breakthroughs. In this paper, in addition to the general challenges, we also discuss domain specific barriers for five different domains i.e., object detection, hardware benchmarking, continual learning, action recognition, and industrial process automation, and highlight the steps necessary for successfully managing the cross-sectoral collaborations between academia and industry

Tackling Key Challenges of AI Development – Insights from an Industry-Academia Collaboration

Harnessing the overall benefits of the latest advancements in artificial intelligence (AI) requires the extensive collaboration of academia and industry. These collaborations promote innovation and growth while enforcing the practical usefulness of newer technologies in real life. The purpose of this article is to outline the challenges faced during cross-collaboration between academia and industry. These challenges are also inspected with the help of an ongoing project titled “Quality Assurance of Machine Learning Applications” (Q-AMeLiA), in which three universities cooperate with five industry partners to make the product risk of AI-based products visible. Further, we discuss the hurdles and the key challenges in machine learning (ML) technology transformation from academia to industry based on robustness, simplicity, and safety. These challenges are an outcome of the lack of common standards, metrics, and missing regulatory considerations when state-of-the-art (SOTA) technology is developed in academia. The use of biased datasets involves ethical concerns that might lead to unfair outcomes when the ML model is deployed in production. The advancement of AI in small and medium sized enterprises (SMEs) requires more in terms of common tandardization of concepts rather than algorithm breakthroughs. In this paper, in addition to the general challenges, we also discuss domain specific barriers for five different domains i.e., object detection, hardware benchmarking, continual learning, action recognition, and industrial process automation, and highlight the steps necessary for successfully managing the cross-sectoral collaborations between academia and industry