Multi-view Human Action Recognition Using Histograms of Oriented Gradients (HOG) Description of Motion History Images (MHIs)
2015 13th International Conference on Frontiers of Information Technology (FIT) (2015)
Dec. 14, 2015 to Dec. 16, 2015
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FIT.2015.59
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.
Cameras, Training, Testing, Histograms, Real-time systems, Image recognition, History,MuHAVi dataset, action recognition, histograms of oriented gradients (HOG), motion history images (MHIs)
Fiza Murtaza, Muhammad Haroon Yousaf, Sergio A. Velastin, "Multi-view Human Action Recognition Using Histograms of Oriented Gradients (HOG) Description of Motion History Images (MHIs)", 2015 13th International Conference on Frontiers of Information Technology (FIT), vol. 00, no. , pp. 297-302, 2015, doi:10.1109/FIT.2015.59