Benchmark RGB-D Gait Datasets: A Systematic Review

Human motion analysis has proven to be a great source of information for a wide range of applications. Several approaches for a detailed and accurate motion analysis have been proposed in the literature, as well as an almost proportional number of dedicated datasets. The relatively recent arrival of depth sensors contributed to an increasing interest in this research area and also to the emergence of a new type of motion datasets. This work focuses on a systematic review of publicly available depth-based datasets, encompassing human gait data which is used for person recognition and/or classification purposes. We have conducted this systematic review using the Scopus database. The herein presented survey, which to the best of our knowledge is the first one dedicated to this type of datasets, is intended to inform and aid researchers on the selection of the most suitable datasets to develop, test and compare their algorithms. (c) Springer Nature Switzerland AG 2019

Monocular Expressive Body Regression through Body-Driven Attention

To understand how people look, interact, or perform tasks, we need to quickly and accurately capture their 3D body, face, and hands together from an RGB image. Most existing methods focus only on parts of the body. A few recent approaches reconstruct full expressive 3D humans from images using 3D body models that include the face and hands. These methods are optimization-based and thus slow, prone to local optima, and require 2D keypoints as input. We address these limitations by introducing ExPose (EXpressive POse and Shape rEgression), which directly regresses the body, face, and hands, in SMPL-X format, from an RGB image. This is a hard problem due to the high dimensionality of the body and the lack of expressive training data. Additionally, hands and faces are much smaller than the body, occupying very few image pixels. This makes hand and face estimation hard when body images are downscaled for neural networks. We make three main contributions. First, we account for the lack of training data by curating a dataset of SMPL-X fits on in-the-wild images. Second, we observe that body estimation localizes the face and hands reasonably well. We introduce body-driven attention for face and hand regions in the original image to extract higher-resolution crops that are fed to dedicated refinement modules. Third, these modules exploit part-specific knowledge from existing face- and hand-only datasets. ExPose estimates expressive 3D humans more accurately than existing optimization methods at a small fraction of the computational cost. Our data, model and code are available for research at https://expose.is.tue.mpg.de .Comment: Accepted in ECCV'20. Project page: http://expose.is.tue.mpg.d

Automated Home-Cage Behavioural Phenotyping of Mice

Neurobehavioral analysis of mouse phenotypes requires the monitoring of mouse behavior over long periods of time. Here, we describe a trainable computer vision system enabling the automated analysis of complex mouse behaviors. We provide software and an extensive manually annotated video database used for training and testing the system. Our system performs on par with human scoring, as measured from ground-truth manual annotations of thousands of clips of freely behaving mice. As a validation of the system, we characterized the home-cage behaviors of two standard inbred and two non-standard mouse strains. From this data we were able to predict in a blind test the strain identity of individual animals with high accuracy. Our video-based software will complement existing sensor based automated approaches and enable an adaptable, comprehensive, high-throughput, fine-grained, automated analysis of mouse behavior.McGovern Institute for Brain ResearchCalifornia Institute of Technology. Broad Fellows Program in Brain CircuitryNational Science Council (China) (TMS-094-1-A032

Biview learning for human posture segmentation from 3D points cloud

Posture segmentation plays an essential role in human motion analysis. The state-of-the-art method extracts sufficiently high-dimensional features from 3D depth images for each 3D point and learns an efficient body part classifier. However, high-dimensional features are memory-consuming and difficult to handle on large-scale training dataset. In this paper, we propose an efficient two-stage dimension reduction scheme, termed biview learning, to encode two independent views which are depth-difference features (DDF) and relative position features (RPF). Biview learning explores the complementary property of DDF and RPF, and uses two stages to learn a compact yet comprehensive low-dimensional feature space for posture segmentation. In the first stage, discriminative locality alignment (DLA) is applied to the high-dimensional DDF to learn a discriminative low-dimensional representation. In the second stage, canonical correlation analysis (CCA) is used to explore the complementary property of RPF and the dimensionality reduced DDF. Finally, we train a support vector machine (SVM) over the output of CCA. We carefully validate the effectiveness of DLA and CCA utilized in the two-stage scheme on our 3D human points cloud dataset. Experimental results show that the proposed biview learning scheme significantly outperforms the state-of-the-art method for human posture segmentation. © 2014 Qiao et al

Aplicación de modelos de mejoramiento de procesos utilizando estudios de tiempos en el área de licitaciones de la empresa Mapfre Seguros

Trabajo de InvestigaciónEl trabajo va orientado a la elaboración de un plan de mejoramiento en un área específica de la empresa Mapfre Seguros. Este objetivo se logró después de hacer un análisis de la situación actual del área a través de varias herramientas de calidad, diagnóstico del proceso del área mediante un estudio de tiempos, y finalmente se realiza el diseño del plan de mejoramiento que permite perfeccionar el proceso del área investigadaINTRODUCCIÓN 1. GENERALIDADES 2. RECOPILACIÓN Y ANÁLISIS DE LA INFORMACIÓN 3. DIAGNÓSTICO DEL ÁREA DE LICITACIONES 4. PROPUESTA DE MEJORAMIENTO 5. CONCLUSIONES 6. RECOMENDACIONES BIBLIOGRAFÍA ANEXOSPregradoIngeniero Industria