Video Based Suspicious Human Behaviour Recognition System

Abstract

In recent years, the number of surveillance cameras installed to monitor private and public spaces has increased rapidly. The demand has raised for smarter video surveillance of public and private spaces using intelligent vision systems which can differentiate between 'suspicious' and 'unsuspicious' behaviours according to the human observer. Generally, the video streams are constantly recorded or monitored by operators. In these cases, an intelligent system can give more accurate performance than a human. We have proposed a method called motion influence map under machine learning for representing human activities. Optical-flow is computed for each pixel in a frame that are processed sequentially. The key feature of the proposed motion influence map is that it effectively reflects the motion characteristics such as movement speed, movement direction, and size of the objects or subjects and their interactions within a frame sequence. It further extracts frames of high motion influence values and compares with the testing frames to automatically detect suspicious activities.