Object detection and tracking based on adaptive canny operator and GM(1,1) model

A new method of moving object detection and tracking from dynamical image sequence is proposed in this paper. This method uses information entropy to achieve the adaptive selection of high threshold in Canny operator. The modified Canny operator is used to detect the object contour from difference images. And this detected contour is then used as the template of partial Hausdorff distance matching. This method predicts the position of moving object in next frame by improved grey prediction model GM(1,1), so it can enhance matching efficiency by taking such position as reference point to locate the search region. The experimental results show that compared with alpha-beta-gamma filter, the grey prediction model GM(1,1) can make error smaller and work stably under information-limited situation, so the grey prediction trajectory can reflect the trend of moving object more accurately and is closer to the actual motion trajectory.