An improved TLD with Harris corner and color moment

Video tracking is a main field of computer vision, and TLD algorithm plays a key role in long-term tracking. However, the original TLD ignores the color features of patch in detection, and tracks the common points from grid, then, the tracking accuracy is limited to both of them. This paper presents a novel TLD algorithm with Harris corner and color moment to overcome this drawback. Instead of tracking common points, we screen more important points utilizing Harris corner to reject a half patches, these points are better able to show the object’s textural features. In addition, the color moment classifier replaces patch variance to reduce the errors of detection. The classifier compares mine-dimensional color moment vectors so that it can keep the TLD’s stable speed. Experiment has proved that our TLD tracks a more reliable position and higher ability without affecting the speed.

[1]  Zdenek Kalal,et al.  Tracking-Learning-Detection , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Ming-Hsuan Yang,et al.  Incremental Learning for Robust Visual Tracking , 2008, International Journal of Computer Vision.

[3]  Eun-Ae Park,et al.  Tracking-Learning-Detection Adopted Unsupervised Learning Algorithm , 2015, 2015 Seventh International Conference on Knowledge and Systems Engineering (KSE).

[4]  Jiri Matas,et al.  Forward-Backward Error: Automatic Detection of Tracking Failures , 2010, 2010 20th International Conference on Pattern Recognition.

[5]  B. D. Lucas.  Generalized image matching by the method of differences , 1985 .

[6]  Horst Bischof,et al.  Real-Time Tracking via On-line Boosting , 2006, BMVC.

[7]  Lei Zhang,et al.  Real-Time Compressive Tracking , 2012, ECCV.

[8]  Deva Ramanan,et al.  Self-Paced Learning for Long-Term Tracking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Jiri Matas,et al.  P-N learning: Bootstrapping binary classifiers by structural constraints , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Steven A. Shafer,et al.  Anatomy of a color histogram , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Yi Wu,et al.  Online Object Tracking: A Benchmark , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Zhi-Hua Zhou,et al.  Multi-instance multi-label learning , 2008, Artif. Intell..

[13]  Baigen Cai,et al.  A Tracking-Learning-Detection (TLD) method with local binary pattern improved , 2015, 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO).