Study to Find Optimal Solution for Multi-objects Detection by Background Image Subtraction with CNN in Real-Time Surveillance System

Abstract

Now a day, video surveillance has gained the most attention in real-time applications. Because of the improvement in domain of machine learning, various methodologies have been designed for multi-object detection. The real-time surveillance systems are the need of various organization as security; hence, this area of research has important. This paper introduces a new methodology for Moving object detection. In the area of real-time video surveillance systems, multi-object detection and object tracking are an important step in video processing. The moving system is critical for many of the application such as operational robot, surveillance systems in military. These real-time surveillance systems are more useful for intelligence gathering, security issues, and many personal needs. Different techniques are used for this task and research is vastly done to make this system automated and to make it reliable. In this research, subjective quality assessment of object detection and object tracking is discussed in detail. In the proposed system, the background subtraction is done from the clean original image and use of CNN with this. We have applied background subtraction method with different filtering algorithms. We have implemented and tried MoD2 and KNN algorithm on the test video and fetched the foreground image. After this, we have applied CNN to track the object. After tracking the objects, we have found out the accuracy with background subtraction method and without background subtraction method. The proposed system improved by approximately 2% as compared to existing techniques.