RT Conference Proceedings
T1 Multi-view Human Action Recognition using Histograms of Oriented Gradients (HOG) Description of Motion History Images (MHIs)
A1 Murtaza, Fiza
A1 Yousaf, Muhammad Haroon
A1 Velastin Carroza, Sergio Alejandro
AB In this paper, a silhouette-based view-independent human action recognition scheme is proposed for multi-camera dataset. To overcome the high-dimensionality issue, incurred due to multi-camera data, the low-dimensional representation based on Motion History Image (MHI) was extracted. A single MHI is computed for each view/action video. For efficient description of MHIs Histograms of Oriented Gradients (HOG) are employed. Finally the classification of HOG based description of MHIs is based on Nearest Neighbor (NN) classifier. The proposed method does not employ feature fusion for multi-view data and therefore this method does not require a fixed number of cameras setup during training and testing stages. The proposed method is suitable for multi-view as well as single view dataset as no feature fusion is used. Experimentation results on multi-view MuHAVi-14 and MuHAVi-8 datasets give high accuracy rates of 92.65% and 99.26% respectively using Leave-One-Sequence-Out (LOSO) cross validation technique as compared to similar state-of-the-art approaches. The proposed method is computationally efficient and hence suitable for real-time action recognition systems.
PB IEEE
SN 978-1-4673-9666-0/15
YR 2015
FD 2015-12
LK https://hdl.handle.net/10016/28945
UL https://hdl.handle.net/10016/28945
LA eng
NO This paper has been presented at : 13th International Conference on Frontiers of Information Technology (FIT)
NO S.A. Velastin acknowledges funding from the Universidad Carlos III de Madrid, the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement n° 600371, el Ministerio de Economia y Competitividad (COFUND2013-51509) and Banco Santander.
DS e-Archivo
RD 30 jun. 2024