Driving Activity Recognition Using UWB Radar and Deep Neural Networks.

In-car activity monitoring is a key enabler of various automotive safety functions. Existing approaches are largely based on vision systems. Radar, however, can provide a low-cost, privacy-preserving alternative. To this day, such systems based on the radar are not widely researched. In our work, we introduce a novel approach that uses the Doppler signal of an ultra-wideband (UWB) radar as an input to deep neural networks for the classification of driving activities. In contrast to previous work in the domain, we focus on generalization to unseen persons and make a new radar driving activity dataset (RaDA) available to the scientific community to encourage comparison and the benchmarking of future methods.

Unsupervised Image-to-Image Translation: A Review.

Supervised image-to-image translation has been proven to generate realistic images with sharp details and to have good quantitative performance. Such methods are trained on a paired dataset, where an image from the source domain already has a corresponding translated image in the target domain. However, this paired dataset requirement imposes a huge practical constraint, requires domain knowledge or is even impossible to obtain in certain cases. Due to these problems, unsupervised image-to-image translation has been proposed, which does not require domain expertise and can take advantage of a large unlabeled dataset. Although such models perform well, they are hard to train due to the major constraints induced in their loss functions, which make training unstable. Since CycleGAN has been released, numerous methods have been proposed which try to address various problems from different perspectives. In this review, we firstly describe the general image-to-image translation framework and discuss the datasets and metrics involved in the topic. Furthermore, we revise the current state-of-the-art with a classification of existing works. This part is followed by a small quantitative evaluation, for which results were taken from papers.

Advanced Scene Perception for Augmented Reality.

Augmented reality (AR), combining virtual elements with the real world, has demonstrated impressive results in a variety of application fields and gained significant research attention in recent years due to its limitless potential [...].

TIMo-A Dataset for Indoor Building Monitoring with a Time-of-Flight Camera.

We present TIMo (Time-of-flight Indoor Monitoring), a dataset for video-based monitoring of indoor spaces captured using a time-of-flight (ToF) camera. The resulting depth videos feature people performing a set of different predefined actions, for which we provide detailed annotations. Person detection for people counting and anomaly detection are the two targeted applications. Most existing surveillance video datasets provide either grayscale or RGB videos. Depth information, on the other hand, is still a rarity in this class of datasets in spite of being popular and much more common in other research fields within computer vision. Our dataset addresses this gap in the landscape of surveillance video datasets. The recordings took place at two different locations with the ToF camera set up either in a top-down or a tilted perspective on the scene. Moreover, we provide experimental evaluation results from baseline algorithms.

Nonlinear Optimization of Light Field Point Cloud.

The problem of accurate three-dimensional reconstruction is important for many research and industrial applications. Light field depth estimation utilizes many observations of the scene and hence can provide accurate reconstruction. We present a method, which enhances existing reconstruction algorithm with per-layer disparity filtering and consistency-based holes filling. Together with that we reformulate the reconstruction result to a form of point cloud from different light field viewpoints and propose a non-linear optimization of it. The capability of our method to reconstruct scenes with acceptable quality was verified by evaluation on a publicly available dataset.

Mixed reality applications in urology: Requirements and future potential.

BACKGROUND: Mixed reality (MR), the computer-supported augmentation of a real environment with virtual elements, becomes ever more relevant in the medical domain, especially in urology, ranging from education and training over surgeries. We aimed to review existing MR technologies and their applications in urology. METHODS: A non-systematic review of current literature was performed using the PubMed-Medline database using the medical subject headings (MeSH) term "mixed reality", combined with one of the following terms: "virtual reality", "augmented reality", ''urology'' and "augmented virtuality". The relevant studies were utilized. RESULTS: MR applications such as MR guided systems, immersive VR headsets, AR models, MR-simulated ureteroscopy and smart glasses have enormous potential in education, training and surgical interventions of urology. Medical students, urology residents and inexperienced urologists can gain experience thanks to MR technologies. MR applications are also used in patient education before interventions. CONCLUSIONS: For surgical support, the achievable accuracy is often not sufficient. The main challenges are the non-rigid nature of the genitourinary organs, intraoperative data acquisition, online and multimodal registration and calibration of devices. However, the progress made in recent years is tremendous in all respects and the gap is constantly shrinking.

SynPo-Net-Accurate and Fast CNN-Based 6DoF Object Pose Estimation Using Synthetic Training.

Estimation and tracking of 6DoF poses of objects in images is a challenging problem of great importance for robotic interaction and augmented reality. Recent approaches applying deep neural networks for pose estimation have shown encouraging results. However, most of them rely on training with real images of objects with severe limitations concerning ground truth pose acquisition, full coverage of possible poses, and training dataset scaling and generalization capability. This paper presents a novel approach using a Convolutional Neural Network (CNN) trained exclusively on single-channel Synthetic images of objects to regress 6DoF object Poses directly (SynPo-Net). The proposed SynPo-Net is a network architecture specifically designed for pose regression and a proposed domain adaptation scheme transforming real and synthetic images into an intermediate domain that is better fit for establishing correspondences. The extensive evaluation shows that our approach significantly outperforms the state-of-the-art using synthetic training in terms of both accuracy and speed. Our system can be used to estimate the 6DoF pose from a single frame, or be integrated into a tracking system to provide the initial pose.

Graphics and Media Technologies for Operators in Industry 4.0.

Visual computing technologies have an important role in manufacturing and production, particularly in new Industry 4.0 scenarios with intelligent machines, human-robot collaboration and learning factories. In this article, we explore challenges and examples on how the fusion of graphics, vision and media technologies can enhance the role of operators in this new context.