Abstract:
I proposed an intelligent system algorithm to address real-time violence activity using
computer vision. Sometimes in our absent different violence activity occurs in our daily
life. As a part of a smart surveillance system detecting real-time violent activity plays a
key role. A video is several frames of the pixel so analyzing and classify them is a
challenging research topic in the field of computer vision. Deep learning nevertheless CNN
is the key part of computer vision. In previous research action recognition mostly focus on
real-life activities but not enough for predicting violence. Considering all possible situation
to recognize real-life violence more accurately in this research I follow Convolutional Long
Short-Term Memory (CONVLSTM). The model finds spatial features from video and
analysis the correlation. Datasets collected from various source and comparatively I get an
adequate accuracy result. The research project finished with several experiments using
different deep video analyzing algorithms. I compared and differentiated different deep
learning model and finalize the best one which about 90% accuracy result. Finally, realtime video footage set to classify with my trained model. The model returns the relevant
output whether the scenario is violent or not at the same time the result sent through the
cloud to my developed mobile application for further action.