Going Deeper into Action Recognition: A Survey

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader

Dauruxu : detección de emociones de personas y sus actividades para el apoyo en la evaluación de factores de riesgo psicosocial

La evaluación de riesgos psicosociales ha desempeñado un papel dominante para garantizar el bienestar y la salud de las personas. No obstante, mecanismos como entrevistas y cuestionarios son susceptibles de obtener resultados sesgados debido a la falta de datos que no se pueden adquirir durante las evaluaciones. Este trabajo propone una arquitectura para identificar actividades y emociones implícitas en los cuestionarios actuales y que tienen el potencial de ser detectadas por cámaras. Mediante visión por computadora, se extraen características de los fotogramas de video los cuales son empleados como predictores para tareas de clasificación. La cuantificación de indicadores basada en la detección de actividades y emociones brindará datos adicionales para respaldar las evaluaciones de riesgo psicosocial.Psychosocial risk assessment has played a dominant role in ensuring the well-being and health of people. However, mechanisms such as interviews and questionnaires are susceptible to obtaining biased results due to the lack of data that cannot be acquired during evaluations. This work proposes an architecture to identify activities and emotions implicit in current questionnaires and that have the potential to be detected by cameras. Through computer vision, features are extracted from the video frames which are used as predictors for classification tasks. The quantification of indicators based on the detection of activities and emotions will provide additional data to support psychosocial risk assessments.Magíster en Ingeniería de Sistemas y ComputaciónMagíster en Analítica para la Inteligencia de NegociosMaestrí