3 research outputs found
Motion-Based Sign Language Video Summarization using Curvature and Torsion
An interesting problem in many video-based applications is the generation of
short synopses by selecting the most informative frames, a procedure which is
known as video summarization. For sign language videos the benefits of using
the -parameterized counterpart of the curvature of the 2-D signer's wrist
trajectory to identify keyframes, have been recently reported in the
literature. In this paper we extend these ideas by modeling the 3-D hand motion
that is extracted from each frame of the video. To this end we propose a new
informative function based on the -parameterized curvature and torsion of
the 3-D trajectory. The method to characterize video frames as keyframes
depends on whether the motion occurs in 2-D or 3-D space. Specifically, in the
case of 3-D motion we look for the maxima of the harmonic mean of the curvature
and torsion of the target's trajectory; in the planar motion case we seek for
the maxima of the trajectory's curvature. The proposed 3-D feature is
experimentally evaluated in applications of sign language videos on (1)
objective measures using ground-truth keyframe annotations, (2) human-based
evaluation of understanding, and (3) gloss classification and the results
obtained are promising.Comment: This work has been submitted to the IEEE for possible publication.
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Detecci贸n de situaciones de violencia f铆sica interpersonal en videos usando t茅cnicas de aprendizaje profundo
Dise帽a una arquitectura
con el modelo de red neuronal convolucional Xception y LSTM para la detecci贸n de violencia
f铆sica interpersonal en los videos de sistemas de vigilancia. Debido al aumento de inseguridad en el pa铆s y como medida preventiva, se busc贸 reforzar el sistema
de videovigilancia, donde se enfoc贸 en la necesidad de integrar nuevas tecnolog铆as para supervisar
la seguridad ciudadana como es el caso del uso de la visi贸n artificial. Para el entrenamiento, validaci贸n y
prueba de la arquitectura del modelo propuesto, se utiliz贸 los conjuntos de datos Hockey Fight
Dataset y Real Life Violence Situations Dataset. Los resultados obtenidos en la exactitud de
nuestra propuesta en el conjunto de datos Hockey Fight Dataset supero a todos los dem谩s m茅todos.
En el caso del conjunto de datos Real Life Violence Situations Dataset que cuenta 2000 videos en
contraste de otros conjuntos de datos utilizados para la detecci贸n de violencia, se obtuvieron
buenos resultados en la exactitud mayores al 90%.Per煤. Universidad Nacional Mayor de San
Marcos. Vicerrectorado de Investigaci贸n y
Posgrado. Proyectos de Investigaci贸n con
Financiamiento para Grupos de Investigaci贸n.
PCONFIGI. C贸digo: C21201361.
Resoluci贸n: 005753-2021-R/UNMS
Surveillance video summarization based on trajectory rarity measure
The dynamic video summarization of surveillance videos has several critical applications, mainly due to the wide availability of digital cameras in environments such as airports, train and bus stations, shopping centers, stadiums, buildings, schools, hospitals, roads, among others. This study presents an approach for the generation of dynamic summary on surveillance video domain based on human trajectories. It has an emphasis on trajectory descriptors in conjunction with the unsupervised clustering method. Our approach contribute to existing literature concerning the combination of methods and objectives. We hypothesize that the clustering of trajectories permits to identify rare trajectories base on their morphology. The clustering as an output provides numerous subsets of trajectories or clusters and the number of elements of a specific cluster is used to determine their rarity. Those subsets with few components are rare while the others that have a high number of elements are considered ordinary; therefore, the implications of our study show that is possible to use unsupervised clustering for automatic detection of rare trajectories based on their morphology and with this information segment videos. We experimented with different sets of trajectories segmenting the rare videos from our ground truth.Trabajo de investigaci贸