Dynamic method to optimize memory space requirement in real-time video surveillance using convolution neural network

Abstract

ABSTRACT Real-time video surveillance is one of the most effective ways to observe crime, mischief, and violence. But, most of the recent surveillance system consumes huge memory space to store the video. This article proposed an advanced dynamic video surveillance strategy to utilize minimum memory space with the finest detection of suspicious moving objects. In the proposed system, a convolution neural network (CNN) classifier and frame resolution switching is used to detect the movement of objects with appropriate frame resolution. A high-definition (HD) frame can be recorded dynamically when any movement of a suspicious object is detected. The system records low-quality frames, which are less important. Finally, the improved gradient-based histogram equalization technique is applied to all frames to obtain enhanced suspicious frames. Several real-time imperial tests are conducted and it is observed that the proposed system detects suspicious objects with 98.25% accuracy. Besides, the system consumes 80% less memory storage.