Continuous affect state annotation using a joystick-based user interface

Ongoing research at the DLR (German Aerospace Center) aims to employ affective computing techniques to ascertain the emotional states of users in motion simulators. In this work, a novel user feedback interface employing a joystick to acquire subjective evaluation of the affective experience is presented. This interface allows the subjects to continuously annotate their affect states, elicited in this scenario by watching video clips. Several physiological parameters (e.g. heart rate, electrodermal activity, respiration rate, etc.) were acquired during the viewing session. A statistical analysis is presented, which shows expected patterns in data that validate the design and methodology of the experiment and lay the groundwork for further experiments to be undertaken at the DLR

A multi-viewpoint feature-based re-identification system driven by skeleton keypoints

Thanks to the increasing popularity of 3D sensors, robotic vision has experienced huge improvements in a wide range of applications and systems in the last years. Besides the many benefits, this migration caused some incompatibilities with those systems that cannot be based on range sensors, like intelligent video surveillance systems, since the two kinds of sensor data lead to different representations of people and objects. This work goes in the direction of bridging the gap, and presents a novel re-identification system that takes advantage of multiple video flows in order to enhance the performance of a skeletal tracking algorithm, which is in turn exploited for driving the re-identification. A new, geometry-based method for joining together the detections provided by the skeletal tracker from multiple video flows is introduced, which is capable of dealing with many people in the scene, coping with the errors introduced in each view by the skeletal tracker. Such method has a high degree of generality, and can be applied to any kind of body pose estimation algorithm. The system was tested on a public dataset for video surveillance applications, demonstrating the improvements achieved by the multi-viewpoint approach in the accuracy of both body pose estimation and re-identification. The proposed approach was also compared with a skeletal tracking system working on 3D data: the comparison assessed the good performance level of the multi-viewpoint approach. This means that the lack of the rich information provided by 3D sensors can be compensated by the availability of more than one viewpoint

Modeling and frequency domain analysis of nonlinear compliant joints for a passive dynamic swimmer

In this paper we present the study of the mathematical model of a real life joint used in an underwater robotic fish. Fluid-structure interaction is utterly simplified and the motion of the joint is approximated by D\"uffing's equation. We compare the quality of analytical harmonic solutions previously reported, with the input-output relation obtained via truncated Volterra series expansion. Comparisons show a trade-off between accuracy and flexibility of the methods. The methods are discussed in detail in order to facilitate reproduction of our results. The approach presented herein can be used to verify results in nonlinear resonance applications and in the design of bio-inspired compliant robots that exploit passive properties of their dynamics. We focus on the potential use of this type of joint for energy extraction from environmental sources, in this case a K\'arm\'an vortex street shed by an obstacle in a flow. Open challenges and questions are mentioned throughout the document.Comment: 12 p, 5 fig, work in progress, collaborative wor

DPPTAM: Dense Piecewise Planar Tracking and Mapping from a Monocular Sequence

This paper proposes a direct monocular SLAM algorithm that estimates a dense reconstruction of a scene in real-time on a CPU. Highly textured image areas are mapped using standard direct mapping techniques [1], that minimize the photometric error across different views. We make the assumption that homogeneous-color regions belong to approximately planar areas. Our contribution is a new algorithm for the estimation of such planar areas, based on the information of a superpixel segmentation and the semidense map from highly textured areas. We compare our approach against several alternatives using the public TUM dataset [2] and additional live experiments with a hand-held camera. We demonstrate that our proposal for piecewise planar monocular SLAM is faster, more accurate and more robust than the piecewise planar baseline [3]. In addition, our experimental results show how the depth regularization of monocular maps can damage its accuracy, being the piecewise planar assumption a reasonable option in indoor scenarios