Pyramidal Fisher Motion for Multiview Gait Recognition

The goal of this paper is to identify individuals by analyzing their gait. Instead of using binary silhouettes as input data (as done in many previous works) we propose and evaluate the use of motion descriptors based on densely sampled short-term trajectories. We take advantage of state-of-the-art people detectors to define custom spatial configurations of the descriptors around the target person. Thus, obtaining a pyramidal representation of the gait motion. The local motion features (described by the Divergence-Curl-Shear descriptor) extracted on the different spatial areas of the person are combined into a single high-level gait descriptor by using the Fisher Vector encoding. The proposed approach, coined Pyramidal Fisher Motion, is experimentally validated on the recent `AVA Multiview Gait' dataset. The results show that this new approach achieves promising results in the problem of gait recognition.Comment: Submitted to International Conference on Pattern Recognition, ICPR, 201

3D human pose estimation from depth maps using a deep combination of poses

Many real-world applications require the estimation of human body joints for higher-level tasks as, for example, human behaviour understanding. In recent years, depth sensors have become a popular approach to obtain three-dimensional information. The depth maps generated by these sensors provide information that can be employed to disambiguate the poses observed in two-dimensional images. This work addresses the problem of 3D human pose estimation from depth maps employing a Deep Learning approach. We propose a model, named Deep Depth Pose (DDP), which receives a depth map containing a person and a set of predefined 3D prototype poses and returns the 3D position of the body joints of the person. In particular, DDP is defined as a ConvNet that computes the specific weights needed to linearly combine the prototypes for the given input. We have thoroughly evaluated DDP on the challenging 'ITOP' and 'UBC3V' datasets, which respectively depict realistic and synthetic samples, defining a new state-of-the-art on them.Comment: Accepted for publication at "Journal of Visual Communication and Image Representation

Automatic learning of gait signatures for people identification

This work targets people identification in video based on the way they walk (i.e. gait). While classical methods typically derive gait signatures from sequences of binary silhouettes, in this work we explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (i.e. optical flow components). We carry out a thorough experimental evaluation of the proposed CNN architecture on the challenging TUM-GAID dataset. The experimental results indicate that using spatio-temporal cuboids of optical flow as input data for CNN allows to obtain state-of-the-art results on the gait task with an image resolution eight times lower than the previously reported results (i.e. 80x60 pixels).Comment: Proof of concept paper. Technical report on the use of ConvNets (CNN) for gait recognition. Data and code: http://www.uco.es/~in1majim/research/cnngaitof.htm

Progressive search space reduction for human pose estimation

The objective of this paper is to estimate 2D human pose as a spatial configuration of body parts in TV and movie video shots. Such video material is uncontrolled and extremely challenging. We propose an approach that progressively reduces the search space for body parts, to greatly improve the chances that pose estimation will succeed. This involves two contributions: (i) a generic detector using a weak model of pose to substantially reduce the full pose search space; and (ii) employing ‘grabcut ’ initialized on detected regions proposed by the weak model, to further prune the search space. Moreover, we also propose (iii) an integrated spatiotemporal model covering multiple frames to refine pose estimates from individual frames, with inference using belief propagation. The method is fully automatic and self-initializing, and explains the spatio-temporal volume covered by a person moving in a shot, by soft-labeling every pixel as belonging to a particular body part or to the background. We demonstrate upper-body pose estimation by an extensive evaluation over 70000 frames from four episodes of the TV series Buffy the vampire slayer, and present an application to fullbody action recognition on the Weizmann dataset. 1

Fatal underfunding? Explaining COVID-19 mortality in Spanish nursing homes

The COVID-19 pandemic has exerted a disproportionate effect on older European populations living in nursing homes. This article discusses the 'fatal underfunding hypothesis', and reports an exploratory empirical analysis of the regional variation in nursing home fatalities during the first wave of the COVID-19 pandemic in Spain, one of the European countries with the highest number of nursing home fatalities. We draw on descriptive and multivariate regression analysis to examine the association between fatalities and measures of nursing home organisation, capacity and coordination plans alongside other characteristics. We document a correlation between regional nursing home fatalities (as a share of excess deaths) and a number of proxies for underfunding including nursing home size, occupancy rate and lower staff to a resident ratio (proxying understaffing). Our preliminary estimates reveal a 0.44 percentual point reduction in the share of nursing home fatalities for each additional staff per place in a nursing home consistent with a fatal underfunding hypothesis

An effective theory of accelerated expansion

We work out an effective theory of accelerated expansion to describe general phenomena of inflation and acceleration (dark energy) in the Universe. Our aim is to determine from theoretical grounds, in a physically-motivated and model independent way, which and how many (free) parameters are needed to broadly capture the physics of a theory describing cosmic acceleration. Our goal is to make as much as possible transparent the physical interpretation of the parameters describing the expansion. We show that, at leading order, there are five independent parameters, of which one can be constrained via general relativity tests. The other four parameters need to be determined by observing and measuring the cosmic expansion rate only, H(z). Therefore we suggest that future cosmology surveys focus on obtaining an accurate as possible measurement of $H(z)$ to constrain the nature of accelerated expansion (dark energy and/or inflation).Comment: In press; minor changes, results unchange

2D Articulated Human Pose Estimation and Retrieval in (Almost) Unconstrained Still Images

We present a technique for estimating the spatial layout of humans in still images—the position of the head, torso and arms. The theme we explore is that once a person is localized using an upper body detector, the search for their body parts can be considerably simplified using weak constraints on position and appearance arising from that detection. Our approach is capable of estimating upper body pose in highly challenging uncontrolled images, without prior knowledge of background, clothing, lighting, or the location and scale of the person in the image. People are only required to be upright and seen from the front or the back (not side). We evaluate the stages of our approach experimentally using ground truth layout annotation on a variety of challenging material, such as images from the PASCAL VOC 2008 challenge and video frames from TV shows and feature films. We also propose and evaluate techniques for searching a video dataset for people in a specific pose. To this end, we develop three new pose descriptors and compare their classification and retrieval performance to two baselines built on state-of-the-art object detection model

Chagas Disease in the Yucatan Peninsula, Mexico

American trypanosomiasis or Chagas disease is caused by the protozoan Trypanosoma cruzi, which affects a wide variety of hosts including the man, until now treatment options or vaccines developed are not enough to control or prevent infected cases. The main way of transmission is vectorial, through insects of the Reduviidae family, as well by congenital transmission, blood/organ transplants or oral transmission. Chagas disease are considered as endemic in many areas due to the presence and lack of control of insect vectors. Many touristic places in Latin America are located in endemic areas; however, there is a nonexistence of knowledge by touristic service providers about the theme. For that reason, there is a latent risk that tourists who come to vacation in endemic areas are exposed get the infection. The risk factors are well identified, and this allows that well-defined prevention strategies can be established in order to avoid the presentation of cases in visitors to the tourist zones. This chapter aimed to describe the situation of Chagas disease in touristic areas of the Caribbean of America Latina as and to provide a brief review of information that allows visitors to know about the epidemiology and potential risks of this infection