Object Tracking and matching in a Video Stream based on SURF and Wavelet Transform - Nasser , Ekhlas Falih; Abdulla Karim, Abdul Alameer (Iraqi Journal of Science, 2022)
In computer vision, visual object tracking is a significant task for monitoringapplications. Tracking of object type is a matching trouble. In object tracking, onemain difficulty is to select features and build models which are convenient fordistinguishing and tracing the target. The suggested system for continuous featuresdescriptor and matching in video has three steps. Firstly, apply wavelet transform onimage using Haar filter. Secondly interest points were detected from wavelet imageusing features from accelerated segment test (FAST) corner detection. Thirdly thosepoints were descripted using Speeded Up Robust Features (SURF). The algorithmof Speeded Up Robust Features (SURF) has been employed and implemented forobject in video stream tracking and matching. The descriptor of feature in SURF canbe operated by minimizing the space of search for potential points of interest insidethe scale space image pyramid. The tracked interest points that are resulted are morerecurrence and pother free. For dealing with images that contain blurring androtation, SURF is best. Fast corner detector can be employed along SURF method tobuild integral images .The integral images can be used to enhance the speed ofimage matching. The features that are extracted from video images are matchedusing Manhattan distance measure. Apply the algorithm of FAST corner detectionalong SURF descriptor of feature; tracking and matching adequacy is better, fast andmore efficient than Scale Invariant Feature Transform SIFT descriptor. Theexperimental outcomes displayed that the time that SURF could be taken formatching is less than the time that SIFT could be taken ,the SURF accuracy dependson number of key-points which are extracted from each frame. SURF key-points areless than SIFT key-points; therefore, SURF key-points could be considered optimalin the process of matching accuracy.