An intelligent video analytics model for abnormal event detection in online surveillance video

One of the primary tools for monitoring human movement and to prevent unwanted or unintended activities is surveillance camera. Nowadays, security management professional rely heavily on video surveillance to challenge crime and avert negative incidents that impact human society. Large numbers of surveillance camera installations have been increasing dramatically in the private and public sectors to monitor public activities. Video surveillance is one of the most effective methods to guarantee security. Fitting a surveillance camera simply transfers the captured video to security personnel. But the abnormal activities can be identified only by integrating an intelligent system to analyze the videos. Hence, this paper is motivated to design and implement an Intelligent Video Analytics Model (IVAM) also known as Human Object Detection (HOD) method for analyzing and detecting video-based abundant objects and abnormal human activities. IVAM can be deployed along with surveillance cameras in any public places like airport, hospital, shopping malls and railway station to automatically identify any abnormal event. IVAM is experimented with MATLAB software and the results are verified. The performance of IVAM is evaluated by comparing the obtained results with the existing approaches and it is proved that IVAM performs better compared to other contemporary methods in terms of accurately detecting the anomalies with less error rate and high classification accuracy.