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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Implementing accurate and reliable passenger detection and counting system is an important task for the correct distribution of available transport system. The aim of this paper is to develop an accurate computer vision-based system to track and count passengers. The proposed passenger detection system incorporates the ideas of well-established detection techniques and is optimally customised for both indoor and outdoor scenarios. The candidate foreground regions (inside an image) are extracted in the proposed method and are described using the histograms of oriented gradient descriptor. These features are trained and tested using support vector machine classifier and the detected passengers are tracked using a filter. The proposed counting system is used to count passengers automatically when they pass through a virtual line of interest. Accuracies ranging 91.2 percent to 86.24 percent were found for passenger detection using the proposed passenger detection and counting system whereas relative counting errors varied ten percent to thirteen percent.

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 andMuHAVi-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-theart approaches. The proposed method is computationally efficient and hence suitable for real-time action recognition systems.

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