3-D Human Action Recognition by Shape Analysis of Motion Trajectories on Riemannian Manifold

International audienceRecognizing human actions in 3D video sequences is an important open problem that is currently at the heart of many research domains including surveillance, natural interfaces and rehabilitation. However, the design and development of models for action recognition that are both accurate and efficient is a challenging task due to the variability of the human pose, clothing and appearance. In this paper, we propose a new framework to extract a compact representation of a human action captured through a depth sensor, and enable accurate action recognition. The proposed solution develops on fitting a human skeleton model to acquired data so as to represent the 3D coordinates of the joints and their change over time as a trajectory in a suitable action space. Thanks to such a 3D joint-based framework, the proposed solution is capable to capture both the shape and the dynamics of the human body simultaneously. The action recognition problem is then formulated as the problem of computing the similarity between the shape of trajectories in a Riemannian manifold. Classification using kNN is finally performed on this manifold taking advantage of Riemannian geometry in the open curve shape space. Experiments are carried out on four representative benchmarks to demonstrate the potential of the proposed solution in terms of accuracy/latency for a low-latency action recognition. Comparative results with state-of-the-art methods are reported

Ten new insights in climate science 2020 – a horizon scan

Non-technical summary We summarize some of the past year's most important findings within climate change-related research. New research has improved our understanding of Earth's sensitivity to carbon dioxide, finds that permafrost thaw could release more carbon emissions than expected and that the uptake of carbon in tropical ecosystems is weakening. Adverse impacts on human society include increasing water shortages and impacts on mental health. Options for solutions emerge from rethinking economic models, rights-based litigation, strengthened governance systems and a new social contract. The disruption caused by COVID-19 could be seized as an opportunity for positive change, directing economic stimulus towards sustainable investments. Technical summary A synthesis is made of ten fields within climate science where there have been significant advances since mid-2019, through an expert elicitation process with broad disciplinary scope. Findings include: (1) a better understanding of equilibrium climate sensitivity; (2) abrupt thaw as an accelerator of carbon release from permafrost; (3) changes to global and regional land carbon sinks; (4) impacts of climate change on water crises, including equity perspectives; (5) adverse effects on mental health from climate change; (6) immediate effects on climate of the COVID-19 pandemic and requirements for recovery packages to deliver on the Paris Agreement; (7) suggested long-term changes to governance and a social contract to address climate change, learning from the current pandemic, (8) updated positive cost–benefit ratio and new perspectives on the potential for green growth in the short- and long-term perspective; (9) urban electrification as a strategy to move towards low-carbon energy systems and (10) rights-based litigation as an increasingly important method to address climate change, with recent clarifications on the legal standing and representation of future generations. Social media summary Stronger permafrost thaw, COVID-19 effects and growing mental health impacts among highlights of latest climate science

Contribution to the analysis of the predissociated rovibronic structure of the symmetric isotopomers 16O3 and 18O3 of ozone near 10,400 cm-1: 3A2(A20)

The absorption spectrum of ozone was Recorded at low temperatures (down to −135 °C) by high resolution Fourier transform spectrometry and intra cavity laser absorption spectroscopy (ICLAS) near 10,400 cm−1. A preliminary analysis of the rotational structure of the absorption spectra of 16O3 and 18O3 shows that this spectral region corresponds to a superposition of two different electronic transitions, one with a very broad rotational structure, showing for the first time the asymmetric stretching frequency mode ν3 of the electronic state 3A2, the other formed by a completely diffuse band, probably the 201 band of a new transition due to the triplet electronic state 3B2. Predissociation effects induce large broadening of the rotational lines for the transition centered at 10,473 cm−1 identified as the 302 band of the Image electronic transition. The rotational structure cannot be analyzed directly but instead the band contour method was used to confirm the symmetry of the transition and to estimate the spectroscopic constants for the 16O isotopomer. The origin of the band is at 10,473±3 cm−1 and the value of the 16O3(3A2) antisymmetric stretching frequency mode is equal to 460±2 cm−1. We believe that the diffuse band is due to the 3B2 state and is located at about 10,363±3 cm−1 for 16O3 and 10,354±3 cm−1 for 18O3. The isotopic rules confirm the different results obtained for 18O3 and 16O3

SHREC 2019 Track: Online Gesture Recognition

This paper presents the results of the Eurographics 2019 SHape Retrieval Contest track on online gesture recognition. The goal of this contest was to test state-of-the-art methods that can be used to online detect command gestures from hands' movements tracking on a basic benchmark where simple gestures are performed interleaving them with other actions. Unlike previous contests and benchmarks on trajectory-based gesture recognition, we proposed an online gesture recognition task, not providing pre-segmented gestures, but asking the participants to find gestures within recorded trajectories. The results submitted by the participants show that an online detection and recognition of sets of very simple gestures from 3D trajectories captured with a cheap sensor can be effectively performed. The best methods proposed could be, therefore, directly exploited to design effective gesture-based interfaces to be used in different contexts, from Virtual and Mixed reality applications to the remote control of home devices