Open AccessMachine Learning Based Human Activity Recognition in Video Surveillance
Author(s) -
Megha Gupta,
Amarjit Malhotra,
Sarthak Sahlot,
Rishabh Attri,
Rajan Kumar
Publication year - 2021
Publication title -
international journal of next-generation computing
Language(s) - English
Resource type - Journals
eISSN - 2229-4678
pISSN - 0976-5034
DOI - 10.47164/ijngc.v12i4.382
Subject(s) - computer science , anomaly detection , extractor , process (computing) , artificial intelligence , feature (linguistics) , state (computer science) , machine learning , computer vision , pattern recognition (psychology) , engineering , algorithm , linguistics , philosophy , process engineering , operating system
In the current time there is a massive shift of technology from analog to digital. According to previous facts, three quarters of world data was in analog form. But now, approximately it is preserved in digital form. Due to this rapid growth of technology there is a massive growth of image data being generated by surveillance cameras. Automated anomaly detection has become necessary in order to detect the presence of any kind of dangerous activities such as robbery, road accidents and many more. Recently, machine Learning approaches have achieved the state of the art results in many tasks related to the automated anomaly detection process. The objective of this paper is to propose one such efficient method for anomaly detection. The proposed approach works with multiple instance learning with I3D feature extractor and crop augmented images. The obtained AUC prove the proposed approach to be superior of previous models.