Advance video analysis system and its applications

This research aims at developing an Advance Video Analysis System (AVAS) which can be used in wide range of video surveillance applications as well as to detect moving objects and human beings. The AVAS is able to detect and track interested objects along with human. It recognizes activities in an application environment, such as in a room, supermarket, car, or security checkpoint. Designing a real-time video analysis system is a complex task, as many factors including processing speed, system cost, accuracy, and robustness, need to be carefully balanced. This research has focused these factors at two levels, algorithm level and software level. Background elimination algorithm is proposed in this paper to enhance the performance of Smart Camera systems in changing background and varying lighting condition environment. Among the main features of this research some are, Event Id, Video Id, and Human Id which give detail information about the events, videos and other tracked objects. Finally, the software implementation of AVAS is applied to detect motion and then to trigger alarm for the security purposes. The system will trigger alarm once the motion is detected and when it exceeds the desire threshold value it will give warning to prevent any loss or mass destruction. Finally, we have given a number of recommendations that need to be addressed for the future growth of surveillance technologies and meeting the end-users' diversified and dynamic requirements. © EuroJournals Publishing, Inc. 2010

Human detection and tracking for video surveillance system

Recent research in video surveillance system has shown an increasing focus on creating reliable systems utilizing non-computationally expensive technique for observing humans’ appearance, movements and activities, thus providing analytical information for advanced human behavior analysis and realistic human modeling. In order for the system to function, it requires robust method for detecting human form from a given input of video streams

Factors affecting systems engineering complexity during developmental phase: systems practitioners, developers, and researchers' perspectives

The Systems Engineering design process is challenged to deliver successful complex systems in multidisciplinary and heterogeneous components. Growing human needs and evolving society bring ever greater challenges in the formation of a complex and large engineered system. System complexity is related to lots of parts and large size of the system when there is difficulty in understanding how the system works or in predicting the consequences of any change that may affect the process and systems develop itself. The leadership is performing an important role to manage a complex system. Leaders should be able to set back from immediate focus and look at the desired big picture. In practice, many factors contribute to Systems Engineering complexity in this review. This study intends to explore and analyze the complexities and the factors that contribute to the complexity of the Systems Engineering design approach. The data in this study were collected systematically from several electronic scholarly databases, including the ISI Web of Science, Scopus, Wiley Online Library. This study quantified the challenges and causes of the Systems Engineer complexity. Then, the challenges were categorized into two groups, managerial and technical causes. Ultimately, seven Systems Engineering complexity factors were identified, and their impact on the Systems Engineering processes was ranked using the Pareto principle. Among the factors, rapidly emerging technology was the most significant factor contributing to Systems Engineering complexity

Development of a flexible video analysis system for motion detection and alarm generation

This paper aims at developing a flexible video analysis system that can be used in wide range of video surveillance applications as well as to detect the human being. The developed system is called here as Smart Video Analysis System. This SVAS is able to detect and track interested objects. It can also detect people and recognize their activities in an application environment, such as in a room, supermarket, car, or security checkpoint. Designing a real-time video analysis system is a complex task, as many factors including processing speed, system cost, accuracy, and robustness, need to be carefully balanced. This research has focused these factors at two levels, algorithm level and software level. Background elimination algorithm is proposed in this paper to enhance the performance of Smart Camera systems in changing background and varying lighting condition environment. Finally, the software implementation of the Smart Camera Analysis Systems is applied to detect motion and then to trigger alarm for the security purposes. The system will trigger alarm once the motion is detected and when it exceeds the desire threshold value. © 2010 IEEE