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.

[1]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[2]  Umut Uludag,et al.  On identification from periocular region utilizing SIFT and SURF , 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

[3]  Vincent Lepetit,et al.  A fast local descriptor for dense matching , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  金出 武雄,et al.  Picture processing system by computer complex and recognition of human faces , 1974 .

[5]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[6]  David E. Culler,et al.  Design and implementation of a high-fidelity AC metering network , 2009, 2009 International Conference on Information Processing in Sensor Networks.

[7]  Ian F. Akyildiz,et al.  Wireless sensor and actor networks: research challenges , 2004, Ad Hoc Networks.

[8]  Zachary Dodds,et al.  Evaluating the Roomba: A low-cost, ubiquitous platform for robotics research and education , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[9]  Özgür B. Akan,et al.  Event-to-sink reliable transport in wireless sensor networks , 2005, IEEE/ACM Transactions on Networking.

[10]  Thomas S. Huang,et al.  Motion and structure from feature correspondences: a review , 1994, Proc. IEEE.

[11]  Hiroyuki Ohsaki,et al.  Community Structure and Interaction Locality in Social Networks , 2015, J. Inf. Process..

[12]  Yang Yu,et al.  Supporting concurrent applications in wireless sensor networks , 2006, SenSys '06.

[13]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Arnold W. M. Smeulders,et al.  Real-Time Visual Concept Classification , 2010, IEEE Transactions on Multimedia.

[15]  Koen E. A. van de Sande,et al.  Evaluating Color Descriptors for Object and Scene Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Ana Cristina Murillo,et al.  SURF features for efficient robot localization with omnidirectional images , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[17]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[18]  Anton van den Hengel,et al.  Thrift: Local 3D Structure Recognition , 2007, 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007).

[19]  Yong-Ho Moon,et al.  Multiple Object Tracking Using SIFT Features and Location Matching , 2011 .

[20]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[21]  Kida Takuya,et al.  Two Dimensional Pattern Matching for JPEG Images , 2008 .

[22]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[23]  Tamim Asfour,et al.  Combining Harris interest points and the SIFT descriptor for fast scale-invariant object recognition , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[24]  Koike Atsushi,et al.  Performance Comparison Between SIFT and SURF for Feature Points Matching in Dynamic Calibration with Zoom Camera , 2012 .

[25]  Kari Pulli,et al.  Realtime Computer Vision with OpenCV , 2012, ACM Queue.