Cyclic Lorentzian Lie Groups

We consider Lie groups equipped with a left-invariant cyclic Lorentzian metric. As in the Riemannian case, in terms of homogeneous structures, such metrics can be considered as different as possible from bi-invariant metrics. We show that several results concerning cyclic Riemannian metrics do not extend to their Lorentzian analogues, and obtain a full classification of three- and four-dimensional cyclic Lorentzian metrics

Inflated 3D ConvNet context analysis for violence detection

According to the Wall Street Journal, one billion surveillance cameras will be deployed around the world by 2021. This amount of information can be hardly managed by humans. Using a Inflated 3D ConvNet as backbone, this paper introduces a novel automatic violence detection approach that outperforms state-of-the-art existing proposals. Most of those proposals consider a pre-processing step to only focus on some regions of interest in the scene, i.e., those actually containing a human subject. In this regard, this paper also reports the results of an extensive analysis on whether and how the context can affect or not the adopted classifier performance. The experiments show that context-free footage yields substantial deterioration of the classifier performance (2% to 5%) on publicly available datasets. However, they also demonstrate that performance stabilizes in context-free settings, no matter the level of context restriction applied. Finally, a cross-dataset experiment investigates the generalizability of results obtained in a single-collection experiment (same dataset used for training and testing) to cross-collection settings (different datasets used for training and testing)

The Fading of Symmetry Non-restoration at Finite Temperature

The fate of symmetries at high temperature determines the dynamics of the very early universe. It is conceivable that temperature effects favor symmetry breaking instead of restoration. Concerning global symmetries, the non-linear sigma model is analyzed in detail. For spontaneously broken gauge symmetries, we propose the gauge boson magnetic mass as a ``flag'' for symmetry (non)-restoration. We consider several cases: the standard model with one and two Higgs doublets in the perturbative regime, and the case of a strongly interacting Higgs sector. The latter is done in a model independent way with the tools provided by chiral Lagrangians. Our results clearly point towards restoration, a pattern consistent with recent lattice computations for global symmetries. In addition, we explicitly verify $BRST$ invariance for gauge theories at finite temperature.Comment: 28 pages, Latex2e, 28 figures, two typos corrected, conclusions remain unchange

Gait Analysis for Gender Classification in Forensics

Gender Classification (GC) is a natural ability that belongs to the human beings. Recent improvements in computer vision provide the possibility to extract information for different classification/recognition purposes. Gender is a soft biometrics useful in video surveillance, especially in uncontrolled contexts such as low-light environments, with arbitrary poses, facial expressions, occlusions and motion blur. In this work we present a methodology for the construction of a gait analyzer. The methodology is divided into three major steps: (1) data extraction, where body keypoints are extracted from video sequences; (2) feature creation, where body features are constructed using body keypoints; and (3) classifier selection when such data are used to train four different classifiers in order to determine the one that best performs. The results are analyzed on the dataset Gotcha, characterized by user and camera either in motion

Gotcha-I: A Multiview Human Videos Dataset

The growing need of security in large open spaces led to the need to use video capture of people in different context and illumination and with multiple biometric traits as head pose, body gait, eyes, nose, mouth, and further more. All these traits are useful for a multibiometric identification or a person re-identification in a video surveillance context. Body Worn Cameras (BWCs) are used by the police of different countries all around the word and their use is growing significantly. This raises the need to develop new recognition methods that consider multibiometric traits on person re-identification. The purpose of this work is to present a new video dataset called Gotcha-I. This dataset has been obtained using more mobile cameras to adhere to the data of BWCs. The dataset includes videos from 62 subjects in indoor and outdoor environments to address both security and surveillance problem. During these videos, subjects may have a different behavior in videos such as freely, path, upstairs, avoid the camera. The dataset is composed by 493 videos including a set of 180° videos for each face of the subjects in the dataset. Furthermore, there are already processed data, such as: the 3D model of the face of each subject with all the poses of the head in pitch, yaw and roll; and the body keypoint coordinates of the gait for each video frame. It’s also shown an application of gender recognition performed on Gotcha-I, confirming the usefulness and innovativeness of the proposed dataset

AveroBot: An audio-visual dataset for people re-identification and verification in human-robot interaction

Intelligent technologies have pervaded our daily life, making it easier for people to complete their activities. One emerging application is involving the use of robots for assisting people in various tasks (e.g., visiting a museum). In this context, it is crucial to enable robots to correctly identify people. Existing robots often use facial information to establish the identity of a person of interest. But, the face alone may not offer enough relevant information due to variations in pose, illumination, resolution and recording distance. Other biometric modalities like the voice can improve the recognition performance in these conditions. However, the existing datasets in robotic scenarios usually do not include the audio cue and tend to suffer from one or more limitations: most of them are acquired under controlled conditions, limited in number of identities or samples per user, collected by the same recording device, and/or not freely available. In this paper, we propose AveRobot, an audio-visual dataset of 111 participants vocalizing short sentences under robot assistance scenarios. The collection took place into a three-floor building through eight different cameras with built-in microphones. The performance for face and voice re-identification and verification was evaluated on this dataset with deep learning baselines, and compared against audio-visual datasets from diverse scenarios. The results showed that AveRobot is a challenging dataset for people re-identification and verification