Real Time Moving Object Detection Based on Masking Approach

Abstract

Moving object detection has been widely used in intelligent surveillance. This paper proposes a new moving object detection scheme in based on gradient information for real time detection. In the proposed moving object detection scheme, the input and background image are converted into gradient map. Then gradient difference map is calculated and proper masking over the map extracts moving objects. The proposed moving object detection scheme is tested by various video sequences to demonstrate the robustness of moving object detection. Simulation results suggest that the moving object detection scheme embedded with the proposed method are highly robust to illumination change, presence of noise and distracting motions. We observe that the proposed method provides better performance than existing edge based methods in terms of displacement error and false detection because of improved edge localization by new gradient detection and gradient directional masking. Our proposed scheme achieves 25% reduction in displacement error than traditional edge based methods.