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Learning motion-difference features using Gaussian restricted Boltzmann machines for efficient human action recognition

Tran, S., Benetos, E. & Garcez, A. (2014). Learning motion-difference features using Gaussian restricted Boltzmann machines for efficient human action recognition. Paper presented at the 2014 International Joint Conference on Neural Networks (IJCNN), 06-07-2014 - 11-07-2014, Beijing, China. doi: 10.1109/IJCNN.2014.6889945


Learning visual words from video frames is challenging because deciding which word to assign to each subset of frames is a difficult task. For example, two similar frames may have different meanings in describing human actions such as starting to run and starting to walk. In order to associate richer information to vector-quantization and generate visual words, several approaches have been proposed recently that use complex algorithms to extract or learn spatio-temporal features from 3-D volumes of video frames. In this paper, we propose an efficient method to use Gaussian RBM for learning motion-difference features from actions in videos. The difference between two video frames is defined by a subtraction function of one frame by another that preserves positive and negative changes, thus creating a simple spatio-temporal saliency map for an action. This subtraction function removes, by construction, the common shapes and background images that should not be relevant for action learning and recognition, and highlights the movement patterns in space, making it easier to learn the actions from such saliency maps using shallow feature learning models such as RBMs. In the experiments reported in this paper, we used a Gaussian restricted Boltzmann machine to learn the actions from saliency maps of different motion images. Despite its simplicity, the motion-difference method achieved very good performance in benchmark datasets, specifically the Weizmann dataset (98.81%) and the KTH dataset (88.89%). A comparative analysis with hand-crafted and learned features using similar classifiers indicates that motion-difference can be competitive and very efficient.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Computer Science
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