An Object Tracking System Based on SIFT and SURF Feature Extraction Methods

In recent years, Ambient Intelligence (AmI) has attracted increased attention within the advanced technology industry in an effort to modernize and develop a more intelligent and reliable information system. Technologies to detect a specific object in images are expected to further expand to wide range of applications, such as car detection functions for Intelligent Transport System (ITS) and other systems. Computer vision and pattern recognition are emerging fast and will continue to grow together with local feature detection methods. In this paper, we propose an object detection and tracking system which is based on Scale Invariant Feature Transform (SIFT) and Speed Up Robust Features (SURF) feature extraction methods. From the evaluation results, we observe that the accuracy of matched keypoints of SURF algorithm are higher than SIFT.