Action Recognition in Video Using Sparse Coding and Relative Features

This work presents an approach to category-based action recognition in video using sparse coding techniques. The proposed approach includes two main contributions: i) A new method to handle intra-class variations by decomposing each video into a reduced set of representative atomic action acts or key-sequences, and ii) A new video descriptor, ITRA: Inter-Temporal Relational Act Descriptor, that exploits the power of comparative reasoning to capture relative similarity relations among key-sequences. In terms of the method to obtain key-sequences, we introduce a loss function that, for each video, leads to the identification of a sparse set of representative key-frames capturing both, relevant particularities arising in the input video, as well as relevant generalities arising in the complete class collection. In terms of the method to obtain the ITRA descriptor, we introduce a novel scheme to quantify relative intra and inter-class similarities among local temporal patterns arising in the videos. The resulting ITRA descriptor demonstrates to be highly effective to discriminate among action categories. As a result, the proposed approach reaches remarkable action recognition performance on several popular benchmark datasets, outperforming alternative state-of-the-art techniques by a large margin.Comment: Accepted to CVPR 201

Gestión administrativa y Soft Skills en los docentes de las Instituciones Educativas FIC de la provincia de Camaná – Arequipa - 2020

El estudio se abordó desde el enfoque cuantitativo. Tal enfoque parte del paradigma positivista, el objetivo general es determinar de qué manera se relaciona la gestión administrativa y Soft Skills en los docentes de las Instituciones Educativas FIC de la provincia de Camaná –Arequipa - 2020. La hipótesis general que se ha formulado es la siguiente: La gestión administrativa se relaciona significativamente con las Soft Skills en los docentes de las Instituciones Educativas FIC de la provincia de Camaná – Arequipa - 2020. La investigación es de tipo aplicada correlacional, con un diseño no experimental. La población fue de 110 docentes y la muestra fue de 86 docentes de las Instituciones FIC. En relación a los instrumentos de recolección de datos, se han formulado dos instrumentos que han sido validados y comprobados su confiabilidad a través del Alfa de Cronbach, el que corresponde a la variable Gestión Administrativa cuenta con 26 items y el que corresponde a la variable Soft Skills cuenta con 36 items, utilizan la escala ordinal. Los resultados han sido analizados mediante el análisis de frecuencias y el análisis inferencial para conocer el nivel de correlación Rho de Spearman, de esta forma se verifica el cumplimiento de los objetivos. La conclusión es que no existe correlación significativa cuyo nivel de significancia es p=0.058, entre las variables Gestión Administrativa y Soft Skills en los docentes de las Escuela FIC de la provincia de Camaná

Exploring the Potential of Vegetation Indices for Urban Tree Segmentation in Street View Images

Urban forests play a crucial role in the development of cities because of the urban ecosystem services they provide. Previous works have alleviated urban forest monitoring by discriminating tree species and performing tree inventories using street view images and convolutional neural networks. However, the characterization of trees from street-view images remains a challenging task. Determining tree structural parameters has been limited because of inaccurate tree segmentation caused by combined, occluded, or leaf-off trees. Therefore, the current work evaluates the potential of vegetation indices derived from red, green, blue, and synthesized near-infrared and red-edge spectral bands for urban tree segmentation. In particular, we attempt to show whether or not vegetation indices add relevant information to deep neural segmentation networks when there are low fine-tuning training samples. A conditional adversarial network generates red-edge and near-infrared images in urban environments, which retrieve an average structural similarity index of 0.86 and 0.81, respectively. Furthermore, we note that by using appropriate multispectral vegetation indices, one can boost the average intersection over the union between 5.07 % to 13.7 %. Specifically, we suggest the SegFormer segmentation network pre-trained with the CityScapes dataset and Red Edge Modified Simple Ration index for improving urban tree segmentation. However, if no multispectral data is available, the DeepLabV3 network pre-trained with the ADE20k dataset is suggested because it could achieve the best RGB outcomes average IoU value of 0.671. </p