Tracking feature extraction techniques with improved SIFT for video identification

Abstract

This paper presents a method for tracking of object movements and detecting of feature to identify video content using improved Scale-Invariant Feature Transform (SIFT). SIFT can robustly identify objects even among clutter and under partial occlusion, because the SIFT feature descriptor is invariant to uniform scaling, orientation, and also partially invariant to affine distortion and illumination changes. Even if the video drops frames or attacked, our method can extract the features. In our method we detect the video features from tracking the object’s movement and make a dataset with feature sequences to identify video. In contrast to the existing tracking techniques, our method recognized reliable object coordinate. The developed algorithm will be an essential part of a completely tracking and identification system. To evaluate the performance of the proposed approach, we was experimenting with several genres of video. Compare with the original SIFT algorithm, we reducing up to 5 % in processing time was achieved for matching. Also appoint the position of the object area in tracking method make the proposed method automatic, fast and effective.