Back to Search
Start Over
Thief Detection with Deep Learning using Yolo Predictive Analysis
- Source :
- International Journal for Research in Applied Science and Engineering Technology. 9:2005-2011
- Publication Year :
- 2021
- Publisher :
- International Journal for Research in Applied Science and Engineering Technology (IJRASET), 2021.
-
Abstract
- This paper discusses thief detection, which is one of the important applications of suspicious human activity detections. Individual safety is a major concern in our busy scheduling life. The main reason for this concern is an ever-increasing number of activities that pose a threat. A simple closed-circuit television (CCTV) installation system is not sufficient enough because it usually requires a person to be alert and monitoring the cameras always is inefficient. The necessitates for the development of a fully automated security system detects anomalous activities in real-time, and provides instant assistance to the victim. As a consequence, we proposed a framework that examines and detects suspicious human activity from real-time Surveillance video using deep learning techniques and generates an alert if abnormal activity occurs. The method was tested on a dataset with both normal and abnormal activity and yielded better results. Keywords: Thief detection, deep-learning, surveillance video, predictive analysis, yolo.
Details
- ISSN :
- 23219653
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- International Journal for Research in Applied Science and Engineering Technology
- Accession number :
- edsair.doi...........11e06c8f81cf50f5431c858318d9b028