Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table

Development of New Models for Vision-Based Human Activity Recognition

Els mètodes de reconeixement d'accions permeten als sistemes intel·ligents reconèixer accions humanes en vídeos de la vida quotidiana. No obstant, molts mètodes de reconeixement d'accions donen taxes notables d’error de classificació degut a les grans variacions dins dels vídeos de la mateixa classe i als canvis en el punt de vista, l'escala i el fons. Per reduir la classificació incorrecta , proposem un nou mètode de representació de vídeo que captura l'evolució temporal de l'acció que succeeix en el vídeo, un nou mètode per a la segmentació de mans i un nou mètode per al reconeixement d'activitats humanes en imatges fixes.Los métodos de reconocimiento de acciones permiten que los sistemas inteligentes reconozcan acciones humanas en videos de la vida cotidiana. No obstante, muchos métodos de reconocimiento de acciones dan tasas notables de error de clasificación debido a las grandes variaciones dentro de los videos de la misma clase y los cambios en el punto de vista, la escala y el fondo. Para reducir la clasificación errónea, Łproponemos un nuevo método de representación de video que captura la evolución temporal de la acción que ocurre en el video completo, un nuevo método para la segmentación de manos y un nuevo método para el reconocimiento de actividades humanas en imágenes fijas.Action recognition methods enable intelligent systems to recognize human actions in daily life videos. However, many action recognition methods give noticeable misclassification rates due to the big variations within the videos of the same class, and the changes in viewpoint, scale and background. To reduce the misclassification rate, we propose a new video representation method that captures the temporal evolution of the action happening in the whole video, a new method for human hands segmentation and a new method for human activity recognition in still images

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