Reconocimiento en-línea de acciones humanas basado en patrones de RWE aplicado en ventanas dinámicas de momentos invariantes

[EN] This paper presents a methodology for online human action recognition on video sequences. It addresses an efficient approach to use invariant moments as image descriptors, applied in processing silhouettes obtained from depth maps. A quick comparison between size-4 windows (equivalent to 4 frames) is performed by computing the Mahalanobis distance, on one of the invariant moment sequences identified as less sensitive to noise and more stable during movement absence. This approach is used for rapid detection of the idle/motion state, which allows the capture of dynamic growth intervals (windows) for further processing, rescuing from the signal contained their temporal and frequential properties. By applying the Haar wavelet transform, three decomposition levels are used for calculating Relative Wavelet Energy (RWE - Relative Wavelet Energy) and SSC (Slope Sign Change), obtaining 11-dimensional patterns. In experiments, 97 % of 4 movements online-captured were recognized correctly, and 10 movements taken from Muhavi-MAS database were recognized with 94.2 % efficiency[ES] En este trabajo se presenta una metodología para el reconocimiento en-línea de acciones humanas en secuencias de vídeo. Se aborda un enfoque eficiente para el uso de momentos invariantes como descriptores de imagen, aplicados en siluetas obtenidas del procesamiento de mapas de profundidad. Una comparación rápida entre ventanas de tamaño 4 (equivalente a 4 frames) es realizada mediante el cómputo de la distancia de Mahalanobis, sobre una de las secuencias de momentos invariantes identificada como la menos sensible al ruido de captura y la más estable durante ausencia de movimiento. Este enfoque es usado para la detección rápida del estado de parada/movimiento, el cual permite la captura de intervalos (ventanas) de crecimiento dinámico para su posterior procesamiento, rescatando de la señal contenida sus propiedades temporales y frecuenciales. Mediante la aplicación de la transformada Wavelet Haar, tres niveles de descomposición son utilizados para el cómputo de la Energía Relativa Wavelet (RWE - Relative Wavelet Energy) y SSC (Slope Sign Change), obteniendo patrones 11-dimensionales. En experimentos realizados, el 97% de 4 movimientos capturados en-línea fueron reconocidos correctamente, y 10 movimientos tomados de la base de datos Muhavi-MAS fueron reconocidos con 94,2% de efectividad.Este proyecto de investigacion es financiado por el Programa Primeros Proyectos, CNPq/FAPES No. 02/2011 y por el CNPq a traves de beca de doctorado para el primer autor.Romero López, D.; Frizera Neto, A.; Freire Bastos, T. (2014). 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