Adaptive Moving Object Detection Algorithm Based on Background Subtraction and Motion Estimation

Moving object detection plays an important role in video surveillance and object tracking. With a stationary camera it is still a challenging problem to precisely extract moving object from real-time video with dynamic backgrounds. In this work, we develop an efficient moving object detection framework to tackle this problem. First, we utilize adaptive Gaussian mixture model to cope with steadily varying components of backgrounds. Second, inspired by the motion estimation method used in video coding, we propose a motion estimation technique to deal with unsteadily varying components of backgrounds. Exhaustive experimental evaluations on complex scenes show that the proposed method is fast and stable, achieving more than 15% improvement in accuracy compared over existing state-of-the-art algorithms. Keyword: Dynamic Background, Adaptive Gaussian Mixture Model, Background Subtraction, Motion Estimation