Nigerian campuses comprises of diverse culture, ethnicity and religion. Controlling these campuses is a very big deal. Cameras are installed to help security personnel in carrying out these enormous tasks. However, the installed closed circuit television [CCTV] cameras are only used for evidence sourcing rather than prevention of campus vices. With the use of appropriate technique, campus vices can be prevented thus securing the campus. In this research, an Artificial Intelligent (AI) video surveillance system was developed. The system captures, analyses video for any abnormal behaviour and alerts relevant personnel for appropriate required action. The scheme uses crowd surge a crowd analyzer for videos with vulnerability and threats. The result shows that when deployed at strategic flagged locations early campus vices are detected and reported to relevant personnel whom take appropriate actions and measures to curb escalation of the vices.

Surveillance videos provide security and increases work efficiency in places of work and homes. as the most acceptable form of evidence, surveillance videos are now tampered to hide actions or convey wrong information. Researchers have proposed ways to mitigate the effect of activities of the attackers through checking the authenticity of the video. The proposed schemes suffer performance degradation in the presence of scene changes. Recently a scheme that addresses the effects of scene change on inter-frame forgery detection was developed where it detects scene changes and divides multiple scenes in to shots. The scheme improves the overall performance of the inter-frame forgery detection at the expense of high average computational time. In this research, a video scene change aware forgery detection scheme is proposed to mitigate the effect of scene change on inter-frame forgery detection with low average computational time. The proposed scheme utilizes the luminance level within frame region which is a more efficient feature to detect scene change. The experimental results show that the scheme has 57% decreases in computational average time and increased in accuracy to 99.03%.