ORACLE: Occlusion-Resilient and Self-Calibrating mmWave Radar Network for People Tracking

Millimeter wave (mmWave) radar sensors are emerging as valid alternatives to cameras for the pervasive contactless monitoring of people in indoor spaces. However, commercial mmWave radars feature a limited range (up to $6$-$8$ m) and are subject to occlusion, which may constitute a significant drawback in large, crowded rooms characterized by a challenging multipath environment. Thus, covering large indoor spaces requires multiple radars with known relative position and orientation and algorithms to combine their outputs. In this work, we present ORACLE, an autonomous system that (i) integrates automatic relative position and orientation estimation from multiple radar devices by exploiting the trajectories of people moving freely in the radars' common fields of view, and (ii) fuses the tracking information from multiple radars to obtain a unified tracking among all sensors. Our implementation and experimental evaluation of ORACLE results in median errors of $0.12$ m and $0.03^\circ$ for radars location and orientation estimates, respectively. Fused tracking improves the mean target tracking accuracy by $27\%$, and the mean tracking error is $23$ cm in the most challenging case of $3$ moving targets. Finally, ORACLE does not show significant performance reduction when the fusion rate is reduced to up to 1/5 of the frame rate of the single radar sensors, thus being amenable to a lightweight implementation on a resource-constrained fusion center

Efficient Time and Space Representation of Uncertain Event Data

Process mining is a discipline which concerns the analysis of execution data of operational processes, the extraction of models from event data, the measurement of the conformance between event data and normative models, and the enhancement of all aspects of processes. Most approaches assume that event data is accurately capture behavior. However, this is not realistic in many applications: data can contain uncertainty, generated from errors in recording, imprecise measurements, and other factors. Recently, new methods have been developed to analyze event data containing uncertainty; these techniques prominently rely on representing uncertain event data by means of graph-based models explicitly capturing uncertainty. In this paper, we introduce a new approach to efficiently calculate a graph representation of the behavior contained in an uncertain process trace. We present our novel algorithm, prove its asymptotic time complexity, and show experimental results that highlight order-of-magnitude performance improvements for the behavior graph construction.Comment: 34 pages, 16 figures, 5 table

SFINGE 3D: A novel benchmark for online detection and recognition of heterogeneous hand gestures from 3D fingers' trajectories

In recent years gesture recognition has become an increasingly interesting topic for both research and industry. While interaction with a device through a gestural interface is a promising idea in several applications especially in the industrial field, some of the issues related to the task are still considered a challenge. In the scientific literature, a relevant amount of work has been recently presented on the problem of detecting and classifying gestures from 3D hands' joints trajectories that can be captured by cheap devices installed on head-mounted displays and desktop computers. The methods proposed so far can achieve very good results on benchmarks requiring the offline supervised classification of segmented gestures of a particular kind but are not usually tested on the more realistic task of finding gestures execution within a continuous hand tracking session.In this paper, we present a novel benchmark, SFINGE 3D, aimed at evaluating online gesture detection and recognition. The dataset is composed of a dictionary of 13 segmented gestures used as a training set and 72 trajectories each containing 3-5 of the 13 gestures, performed in continuous tracking, padded with random hand movements acting as noise. The presented dataset, captured with a head-mounted Leap Motion device, is particularly suitable to evaluate gesture detection methods in a realistic use-case scenario, as it allows the analysis of online detection performance on heterogeneous gestures, characterized by static hand pose, global hand motions, and finger articulation.We exploited SFINGE 3D to compare two different approaches for the online detection and classification, one based on visual rendering and Convolutional Neural Networks and the other based on geometrybased handcrafted features and dissimilarity-based classifiers. We discuss the results, analyzing strengths and weaknesses of the methods, and deriving useful hints for their improvement. (C) 2020 Elsevier Ltd. All rights reserved