The Need for Marker-Less Computer Vision Techniques for Human Gait Analysis on Video Surveillance to Detect Concealed Firearms

Crimes involving the use of firearms have been on the increase in the past few years. One of the measures adopted to prevent these crimes is the use of CCTV operators at video surveillance centers to detect persons carrying concealed firearms on their bodies by monitoring their behavior. This paper has found that this technique has challenges associated with human weaknesses and errors. A review of the current attempts to automate video surveillance for concealed firearm detection has found that they have the limitation that the techniques can only be employed on stationary and cooperative persons. This makes them inappropriate for real-life surveillance. This paper highlights the need for automated video surveillance solutions that can detect persons carrying concealed firearms when they are not stationary and aware of the scanning process. We further explore automated behavioral analysis and specifically gait analysis as a possible technique for concealed firearm detection on video surveillance. Lastly, the paper highlights the possibility and viability of human gait analysis using marker-less computer vision techniques for detecting persons carrying firearms on their waist line

Improving the accessibility of digital content via mobile technology. A case study of Mount Kenya University

Globally, Higher Education Institutions (HEI) have embraced the use of mobile technology in the delivery of instructional resources which has promised multiple benefits in digital or blended learning, HEIs are facing the challenge of high internet tariffs. The current study sought to improve the accessibility of digital content via mobile technology within limited Internet connectivity contexts. The study used a quantitative research approach within which a descriptive survey research design was adopted. The case study was Mount Kenya University in Kenya. The study was guided by the Technology Acceptance Model (TAM). The target population was 15123 individuals comprising of 15,000 students and 123 were educators/ ICT staff who accessed digital content in the academic year 2018/2019. The mobile-based model used a WIFI router device which is not internet supported as an alternative to a wired internet connection where students and educators access digital content from the mobile sub-server which was not connected to the internet through their mobile technology. The findings showed that there is a statistically significant relationship between internet connectivity, type of mobile technology, user literacy, data caching, and eLearning policy had a significant effect on the accessibility of digital content. The variables were statistically significant. The adjusted R squared was 0.862 indicating that 86.2 percent of the total variation of accessibility of digital content can be explained by Internet connectivity, e-learning policy, type of mobile technology, data caching, and user literacy. The study then went ahead to develop a mobile-based e-learning model. The findings showed that the use of mobile-based e-learning (m-learning) in universities will greatly improve access to digital content and hence e-learning. The study recommends the use of m-learning as it will provide alternative means of optimizing Internet connectivity