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Moving foreground object detection via robust SIFT trajectories. Lecture Notes in Computer Science, vol 8695. In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. Dense semi-rigid scene flow estimation from RGB-D images.
#Cinematica tipos de movimientos registration#
An iterative image registration technique with an application to stereo vision, Proceedings of the 7th international joint conference on Artificial intelligence, 1981. Local/global scene flow estimation, 20th IEEE International Conference on Image Processing (ICIP), 2013. Accuracy analysis of kinect depth data, ISPRS workshop laser scanning, 2011. RGB-D flow: Dense 3-d motion estimation using color and depth, 2013 IEEE International Conference on Robotics and Automation (ICRA), 2013. A variational method for scene flow estimation from stereo sequences, 11th IEEE International Conference on Computer Vision ICCV, 2007. Three-dimensional scene flow, Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999. Vedula, S., Baker, S., Rander, P., Collins, R. Real-time human pose recognition in parts from single depth images, 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A. Depth kernel descriptors for object recognition., 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2011. Combing RGB and depth map features for human activity recognition, Signal & Information Processing Association Annual Summit and Conference (APSIPA ASC), Asia-Pacific, 2012.
A learned feature descriptor for object recognition in RGB-D data, 2012 IEEE International Conference on Robotics and Automation (ICRA), 2012. Métricas de Artículo |Resumen : 418 |īlum M., Springenberg, J.T., Wülfing, J. The novel video descriptor based on 3D+t motion trajectories achieved an average accuracy of 80% in a dataset of 5 gestures and 100 videos. Such kinematic words were processed into a bag-of-kinematic-words framework to obtain an occurrence video descriptor. Each motion trajectory models kinematic words primitives that together can describe complex gestures developed along videos. This work presents a novel strategy to compute 3D+t dense and long motion trajectories as fundamental kinematic primitives to represent video sequences. Nevertheless, such strategies only recover motion information among a couple of frames, limiting the analysis of coherent large displacements along time. Regarding motion characterization, typical RGB-D strategies are limited to namely analyze global shape changes and capture scene flow fields to describe local motions in depth sequences. RGB-D sensors have allowed attacking many classical problems in computer vision such as segmentation, scene representations and human interaction, among many others. Este descriptor de trayectorias logró una exactitud del 80% en 5 gestos y 100 videos.
Estas palabras cinemáticas fueron procesadas dentro de un esquema de bolsa-de-palabras para obtener un descriptor basado ocurrencias.
Cada trayectoria permite modelar palabras cinemáticas, las cuales en conjunto, describen gestos complejos en los videos. Este trabajo presenta una estrategia para el cálculo de trayectorias (3D+t), las cuales son fundamentales para la descripción cinemática local, permitiendo una descripción densa de movimiento. Estas estrategias, sin embargo, solo recuperan información dinámica entre cuadros consecutivos, limitando el análisis de largos desplazamientos. Con respecto a la caracterización de movimiento, las estrategias típicas en RGB-D están limitadas al análisis dinámico de formas globales y a la captura de flujos de escena. Los sensores RGB-D han permitido atacar de forma novedosa muchos de los problemas clásicos en visión por computador, tales como la segmentación, la representación de escenas, la interacción humano-computador, entre otros. RGB-D, scene flows, dense motion trajectories, tracking, kinematic features Resumen
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