A Comparative Study of Three Corner Feature based Moving Object Detection Using Aerial Images

Corner feature based moving objects detection is an essential and fundamental research problem in the broader aspects of computer vision and pattern recognition research domain. Performance of various corner features based aerial types image processing specially for moving objects detection is still an unsolved issue due to up and down of performance which makes it difficult to choose the appropriate corner features for detection purpose. The core part mentioned in this research is to categorize significant corner characteristics of the objects using various corner features based detection methods in image extracted from aerial video. This research demonstrated three kinds of corner features, i.e. Moravec, Susan and Harris corners due to capability of these corner features to interpret high and low intensity various for aerial types of images. Standard datasets were used to evaluate each of the corner feature based detection. Based on comprehensive experimental analysis, Harris corner was observed performing efficiently comparing with Moravec and Susan corner based detection for both datasets considered by this research. Experimental results reveals the capacity of each corner characteristics based detection methodology in terms with the effectiveness using various performance metrics for moving object detection using aerial images.