Robust off-line text independent writer identification using bagged discrete cosine transform features

Efficient writer identification systems identify the authorship of an unknown sample of text with high confidence. This has made automatic writer identification a very important topic of research for forensic document analysis. In this paper, we propose a robust system for offline text independent writer identification using bagged discrete cosine transform (BDCT) descriptors. Universal codebooks are first used to generate multiple predictor models. A final decision is then obtained by using the majority voting rule from these predictor models. The BDCT approach allows for DCT features to be effectively exploited for robust hand writer identification. The proposed system has first been assessed on the original version of hand written documents of various datasets and results have shown comparable performance with state-of-the-art systems. Next, blurry and noisy documents of two different datasets have been considered through intensive experiments where the system has been shown to perform significantly better than its competitors. To the best of our knowledge this is the first work that addresses the robustness aspect in automatic hand writer identification. This is particularly suitable in digital forensics as the documents acquired by the analyst may not be in ideal conditions

An Integrated Framework for CCTV Infrastructures Deployment in KSA: Towards an Automated Surveillance

The process of implementation and installation of CCTV has been a sensitive issue across the world, leading to significant political, economic, social and legal implications. Saudi Arabia, like most countries of the world, has been engaged in the deployment of CCTV surveillance systems for many years now. However, the recent developments, as regards stepping up security against possible terror attacks, has led the decision-makers to begin considering the deployment of a significantly large number of cameras in most sensitive public places across the country. The Saudi laws are based on the Sharia Law which clearly states that no picture/video of any Muslim women or men should be taken without their full consent. In the light of this law, the case for extensive networks of CCTV surveillance has proven to be extremely challenging for the decision-makers in the country. Accordingly, two questions have emerged: 1) how to measure the extent of the cultural and traditional values on the deployment of CCTV systems within the Kingdom? 2) How to develop a framework for effective deployment of CCTV systems in KSA? In the light of these questions, the research has, in addition to the examination of the literature, used three case studies and applied a questionnaire and a number of interviews. The application of statistical techniques revealed several useful findings. The potential safety of CCTV, from respondents point of view, turned out to be highly satisfactory, but a large majority of participants were concerned about two issues: privacy invasion, and controllers’ misuse/ abuse of CCTV recordings. The concern about CCTV seems to outweigh the benefits. The study therefore recommends that a nationwide set of regulatory measures need to be developed to incorporate, among many issues, the training programmes for all the control rooms staff, particularly those in charge of public places. Finally, as a potential application of CCTV, the study proposes an automated human detection system based on a Laplacian fitting concept based on human silhouette extraction. With this scheme, a robust human silhouette extraction can be maintained with little human intervention, saving significant amount of man efforts in video processing

Robust Human Silhouette Extraction with Laplacian Fitting

Human silhouette extraction has been a primary step to estimate human poses or classify activities from videos. While the accuracy of human silhouettes has great impact on the follow-on human pose/gait estimation, it has been important to guarantee the highly-accurate extraction of human silhouettes. However, traditional methods such as motion segmentation can be fragile due to the complexity of real-world environment. In this paper, we propose an automated human silhouette extraction algorithm to attain this highly-demanded task. In our proposed scheme, the initial motion segmentation of foreground objects was roughly computed by Stauffer’s background subtraction using Gaussian mixtures, and then refined by the proposed Laplacian fitting scheme. In our method, the candidate regions of human objects are taken as the initial input, their Laplacian matrices are constructed, and Eigen mattes are then obtained by minimizing on Laplacian matrices. RANSAC algorithm is then applied to fit the Eigen mattes iteratively with inliers of the initially estimated motion blob. Finally, the foreground human silhouettes are obtained from the optimized matte fitting. Experimental results on a number of test videos validated that the proposed Laplacian fitting scheme enhances the accuracy in automated human silhouette extraction, exhibiting a potential use of our Laplacian fitting algorithm in many silhouette-based applications such as human pose estimation

Deployment of CCTV in Saudi Arabia: Security, Culture and Religion

Deployment of CCTV surveillance systems has now become a worldwide practice for securing people and businesses alike. A common goal of all CCTV surveillance systems is to detect crime and disorder in a timely manner, enabling the law enforcers to possibly prevent it from happening. The effective deployment of CCTV in Saudi Arabia is of particular interest to researchers and decision-makers as in addition to the usual cons and pros, cultural and religious factors do severely hinder its effective implementation. In particular, as prescribed in the sharia law, men or women are not allowed to take picture/video or acquire picture/video; hence making it very hard to argue in favour of the case for CCTV systems. Based on a simple model of cost-benefit analysis, this study attempts to evaluate the social costs and returns associated with the deployment of CCTV surveillance systems in both public and private places across the country. In so doing, the research has focused on a case study of a large public hospital in Riyadh as a pilot case for evaluation of effectiveness of use of CCTV. Using a large sample of doctors, nurses, workers and patients of the hospital, the study has produced a structured questionnaire survey. The preliminary findings are indicative of several main issues. Firstly, due to lack of education on the part of some patients and workers, over 52% of such participants declared that they had no knowledge about the potential usefulness of the CCTV surveillance in crime reduction. Secondly, a significantly large number of doctors and nurses declared that they were fully supportive of the surveillance systems as they believed it would help reduce theft and provide a safe and secure environment for them to work. Thirdly, although over 50% of participants tend to believe that CCTV systems can help reduce crimes, they were concerned that the staff in charge of such CCTV systems may abuse the power and hence jeopardise the true effectiveness of the system. Finally, according to the initial findings of the study, it is anticipated that there would be more CCTV systems in place in Saudi Arabia as the issue of security tends to overshadow other cultural and religious issues

Efficient segmentation of sub-words within handwritten arabic words

Segmentation is considered as a core step for any recognition or classification method and for the text within any document to be effectively recognized it must be segmented accurately. In this paper a text and writer independent algorithm for the segmentation of sub-words in Arabic words has been presented. The concept is based around the global binarization of an image at various thresholding levels. When each sub-word or Part of Arabic Word (PAW) within the image being investigated is processed at multiple threshold levels a cluster graph is obtained where each cluster represents the individual sub-words of that word. Once the clusters are obtained the task of segmentation is managed by simply selecting the respective cluster automatically which is achieved using the 95% confidence interval on the processed data generated by the accumulated graph. The presented algorithm was tested on 537 randomly selected words from the AHTID/MW database and the results showed that 95.3% of the sub-words or PAW were correctly segmented and extracted. The proposed method has shown considerable improvement over the projection profile method which is commonly used to segment sub-words or PAW

Traffic Flow Estimation from Road Surveillance

Real-time traffic analysis using the road mounted surveillance cameras present multitude of benefits. This kind of traffic video processing has become an important means for intelligent traffic management and control. The estimation and analysis of road traffic motion is an involved task in computer vision and video processing. In our work, morphological operations and region growing method are used to perform salient motion detection of objects. In classical background extraction method, the background has to be learnt from large numbers of frames. In our method, no a prior knowledge about shape and size of object is acquired. Instead, sum of square difference is estimated via online learning for the calculation of the centroid distance. The test results indicate that the road vehicles and their statistics are determined through our algorithm with complete fidelity