Action recognition based on efficient deep feature learning in the spatio-temporal domain

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Hand-crafted feature functions are usually designed based on the domain knowledge of a presumably controlled environment and often fail to generalize, as the statistics of real-world data cannot always be modeled correctly. Data-driven feature learning methods, on the other hand, have emerged as an alternative that often generalize better in uncontrolled environments. We present a simple, yet robust, 2D convolutional neural network extended to a concatenated 3D network that learns to extract features from the spatio-temporal domain of raw video data. The resulting network model is used for content-based recognition of videos. Relying on a 2D convolutional neural network allows us to exploit a pretrained network as a descriptor that yielded the best results on the largest and challenging ILSVRC-2014 dataset. Experimental results on commonly used benchmarking video datasets demonstrate that our results are state-of-the-art in terms of accuracy and computational time without requiring any preprocessing (e.g., optic flow) or a priori knowledge on data capture (e.g., camera motion estimation), which makes it more general and flexible than other approaches. Our implementation is made available.Peer ReviewedPostprint (author's final draft

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

Action recognition based on efficient deep feature learning in the spatio-temporal domain

Hand-crafted feature functions are usually designed based on the domain knowledge of a presumably controlled environment and often fail to generalize, as the statistics of real-world data cannot always be modeled correctly. Data-driven feature learning methods, on the other hand, have emerged as an alternative that often generalize better in uncontrolled environments. We present a simple, yet robust, 2-D convolutional neural network extended to a concatenated 3-D network that learns to extract features from the spatio-temporal domain of raw video data. The resulting network model is used for content-based recognition of videos. Relying on a 2-D convolutional neural network allows us to exploit a pretrained network as a descriptor that yielded the best results on the largest and challenging ILSVRC-2014 dataset. Experimental results on commonly used benchmarking video datasets demonstrate that our results are state-of-the-art in terms of accuracy and computational time without requiring any preprocessing (e.g., optic flow) or a priori knowledge on data capture (e.g., camera motion estimation), which makes it more general and flexible than other approaches. Our implementation is made available.This research is partially funded by the CSIC project TextilRob (201550E028), and the project RobInstruct (TIN2014-58178-R).Peer Reviewe

Action recognition based on efficient deep feature learning in the spatio-temporal domain

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Hand-crafted feature functions are usually designed based on the domain knowledge of a presumably controlled environment and often fail to generalize, as the statistics of real-world data cannot always be modeled correctly. Data-driven feature learning methods, on the other hand, have emerged as an alternative that often generalize better in uncontrolled environments. We present a simple, yet robust, 2D convolutional neural network extended to a concatenated 3D network that learns to extract features from the spatio-temporal domain of raw video data. The resulting network model is used for content-based recognition of videos. Relying on a 2D convolutional neural network allows us to exploit a pretrained network as a descriptor that yielded the best results on the largest and challenging ILSVRC-2014 dataset. Experimental results on commonly used benchmarking video datasets demonstrate that our results are state-of-the-art in terms of accuracy and computational time without requiring any preprocessing (e.g., optic flow) or a priori knowledge on data capture (e.g., camera motion estimation), which makes it more general and flexible than other approaches. Our implementation is made available.Peer Reviewe