In this paper, a new multi-view human action recognition approach is proposed by exploiting low dimensional motion information of actions. Before feature extraction, pre-processing steps are performed to remove noise from silhouettes, incurred due to imperfect but realistic segmentation. 2D motion templates based on Motion History Image (MHI) are computed for each view/action video which can cope with the high-dimensionality issue, incurred due to multi-camera data. Histograms of Oriented Gradients (HOGs) are used as an efficient description of the MHIs. Finally, a Nearest Neighbor (NN) classifier is employed for the classification of the HOG based description of MHIs. As compared to existing approaches, the proposed method has three advantages: 1) does not require a fixed number of cameras setup during training and testing stages hence missing camera-views can be tolerated, 2) requires less memory and bandwidth requirements and hence 3) is computationally efficient which makes it suitable for real-time action recognition. The proposed method is evaluated on the new MuHAVi-uncut dataset having a large number of action categories and a large set of camera-views with noisy silhouettes. As far as we know, this is the first report of results on that dataset and can be used by future workers as a baseline to improve upon. Experimentation results on multi-view with this dataset gives a high accuracy rate of 95.4% using Leave-One-Sequence-Out (LOSO) cross validation technique and compares well to similar state-of-the-art approaches.

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Cryptography is commonly used to secure communication and data transmission over insecure networks through the use of cryptosystems. A cryptosystem is a set of cryptographic algorithms offering security facilities for maintaining more cover-ups. A substitution-box (S-box) is the lone component in a cryptosystem that gives rise to a nonlinear mapping between inputs and outputs, thus providing confusion in data. An S-box that possesses high nonlinearity and low linear and differential probability is considered cryptographically secure. In this study, a new technique is presented to construct cryptographically strong 8×8 S-boxes by applying an adjacency matrix on the Galois field GF(28). The adjacency matrix is obtained corresponding to the coset diagram for the action of modular group PSL(2,Z) on a projective line PL(F7) over a finite field F7. The strength of the proposed S-boxes is examined by common S-box tests, which validate their cryptographic strength. Moreover, we use the majority logic criterion to establish an image encryption application for the proposed S-boxes. The encryption results reveal the robustness and effectiveness of the proposed S-box design in image encryption applications.

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