Suspicious Activity Detection from Surveillance Video using Deep Learning
Author(s):
Shashank Reddy Nallu, Vamshi Krishna Kunuru, Harshavardhan Reddy, Praveen H
Keywords:
Suspicious activity, Video Surveillance, Deep learning, LSTM, Convolutional Neural Networks.
Abstract
Video surveillance has been used since long to ensure security in many sensitive places, so with this great advancement in various aspects of life, traditional surveillance operations face many challenges due to the large amount of information that has to be processed manually in a limited amount. time also the possibility of losing information that may contain important things such as suspicious behavior. Thus, a large amount of research has been conducted in the field of video surveillance recently. We provide a system that supports intelligent monitoring to detect abnormal behavior that poses a security risk. The proposed algorithm is designed to detect two cases of human activity: walking and running. There is no limit to the number of people on stage or the direction of travel. However, video is limited to internal color movies taken from a still camera. A background subtraction algorithm is used to detect moving objects related to people in the scene. We consider the moving speed of the center of the segmented foreground region and the size change speed of the segmented region as two main features to classify the activity v. The proposed algorithm determines the types of activities with high accuracy.
Article Details
Unique Paper ID: 159270

Publication Volume & Issue: Volume 9, Issue 11

Page(s): 855 - 859
Article Preview & Download


Share This Article

Conference Alert

NCSST-2023

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2023

SWEC- Management

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2023

Go To Issue



Call For Paper

Volume 10 Issue 1

Last Date for paper submitting for March Issue is 25 June 2023

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews