Security Threat Object Recognition System
Shubham Kumar1, Shyam.N2, Shaik Sameer3, S.Ruby4, Dr V.Ramesh Babu5, Dr M.Anand6
Shubhamshreyash360@gmail.com, Shyamnaidu2002@gmail.com , Reemas449900@gmail.com ,
Ruby.cse@drmgrdu.ac.in, rameshbabu.cse@drmgrdu.ac.in
1,2,3Undergraduate Student, Department of Computer science and Engineering,
Dr.M.G.R Educational and Research Institute, Tamilnadu, India.
4 Assistant professor, Computer Science and Engineering, Dr.M.G.R Educational and Research Institute, Tamilnadu, India.
5Assistant Professor , Department of Computer science and Engineering, Dr.M.G.R Educational and Research Institute, Tamilnadu, India.
6 Associate professor Additional H.O.D, Department of Computer science and Engineering, Dr.M.G.R Educational and Research Institute, Tamilnadu, India.
ABSTRACT
Security Threat object recognition has emerged as a very advanced topic in the field of video surveillance community. Due to need of system has been proposed for video monitoring or surveillance system for public and private places to protect againt thept and recognise the suspicious object which is harmful for the society and the people. Because of these systems' complexity, researchers frequently handle the various stages of analysis—such as foreground segmentation, stationary object recognition, and abandonment validation—independently. The impact of each level of improvement on the overall performance of the system has not been investigated, despite the fact that each stage has seen gains. This is because the advancements are rarely applied across the entire pipeline. This study presents a thorough assessment of the state-of-the-art methods for each stage and formalizes the framework used by systems for abandoned object detection. Additionally, we develop a multi-configuration system that enables the selection of many options for every step, with the goal of identifying the combination that yields the best results. The scientific community has internet access to this multi-configuration.
Keywords: Object detection, Surveillance, Threat Detection, Computer Vision, Deep Learning, YOLO, Image analysis, CNN, R-CNN, SSD
