Implementation of Covid-19 Social Distance Detection and Suspicious Human Behavior Recognition using Machine Learning
Abstract
Detection of suspicious activities in public transport areas using video surveillance has attracted an increasing level of attention. In general, automated offline video processing systems have been used for post-event analysis, such as forensics and riot investigations. However, very little has been achieved regarding real-time event recognition. In this paper, we introduce a frame- work that processes raw video data received from a fixed color camera installed at a particular location, which makes real- time inferences about the observed activities. These supervised machine learning techniques are used for detection and tracking of Covid-19 social distancing between one or more person’s movements in public places and these observations can be done by the CCTV videos. First, the proposed framework obtains 3-D object-level information by detecting and tracking people and luggage in the scene using a real-time blob matching technique. Based on the temporal properties of these blobs, behaviors and events are semantically recognized by employing object and interobject motion features. A number of types of behavior that are relevant to security in public transport areas have been selected to demonstrate the capabilities of this approach. Examples of these are abandoned and stolen objects, fighting, fainting, and loitering. Using standard public data sets, the experimental results presented here demonstrate the out- standing performance and low computational complexity of this approach.
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