Suspicious Action Detection in Intelligent Surveillance System Using Action Attribute Modelling
Manisha Mudgal, Deepika Punj, Anuradha Pillai
Abstract
Research in the field of image processing and computer vision for recognition of suspicious activity is growing actively. Surveillance systems play a key role in monitoring of sensitive places such as airports, railway stations, shopping complexes, roads, parking areas, roads, banks. For a human it is very difficult to monitor surveillance videos continually, therefore a smart and intelligent system is required that can do real time monitoring of all activities and can categories between usual and some abnormal activities. In this paper many different abnormal activities has been discussed. More focuses is given to violence activity like hitting, slapping, punching etc. For this large human action dataset like UCF101, Kaggel is required. This paper proposes a method to model violence actions using Gaussian Mixture Model with Universal Attribute Model. In this action vector is used to remove redundant attributes and get a low dimensional relevant action vectors.