Citation
Maqueda Nieto, Ana I. and Ruano San Martín, Arturo and Blanco Adán, Carlos Roberto del and Carballeira López, Pablo and Jaureguizar Núñez, Fernando and García Santos, Narciso
(2015).
Novel Multi-Feature Bag-of-Words Descriptor via Subspace Random Projection for Efficient Human-Action Recognition.
In: "12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2015)", 25/08/2015 - 28/08/2015, Karlsruhe, Germany. pp. 1-6.
https://doi.org/10.1109/AVSS.2015.7301736.
Abstract
Human-action recognition through local spatio-temporal features have been widely applied because of their simplicity and its reasonable computational complexity. The most common method to represent such features is the well-known Bag-of-Words approach, which turns a Multiple-Instance Learning problem into a supervised learning one, which can be addressed by a standard classifier. In this paper, a learning framework for human-action recognition that follows the previous strategy is presented. First, spatio-temporal features are detected. Second, they are described by HOG-HOF descriptors, and then represented by a Bag of Words approach to create a feature vector representation. The resulting high dimensional features are reduced by means of a subspace-random-projection technique that is able to retain almost all the original information. Lastly, the reduced feature vectors are delivered to a classifier called Citation K-Nearest Neighborhood, especially adapted to Multiple-Instance Learning frameworks. Excellent results have been obtained, outperforming other state-of-the art approaches in a public database.