Moving Object Detection Based on Temporal Information

Abstract

This letter presents an automatic moving object detection method in image sequences captured from videos. While we focus on extracting moving objects throughout a frame sequence, our approach does not require any prior knowledge such as the background modeling nor the interaction by users such as empirical thresholds tuning. Based on the continuous symmetric difference of the adjacent frames, we get the full resolution saliency map of the current frame, which highlights the moving objects with higher saliency values and meanwhile inhibits the saliency of the background. Then, the maximum entropy sum method is utilized to adaptively calculate the threshold to determine the candidate areas and get the reasonable attention seeds. After that, the ground truth is obtained based on the modified fuzzy growing of the attention seeds. The proposed algorithm is effective, robust and the experimental results demonstrate that it is promising for moving object detection.