Object Tracking Based on Covariance Descriptors and On-Line Naive Bayes Nearest Neighbor Classifier

Object tracking in video sequences has been extensively studied in computer vision. Although promising results have been achieved, often the proposed solutions are tailored for particular objects, structured to specific conditions or constrained by tight guidelines. In real cases it is difficult to recognize these situations automatically because a large number of parameters must be tuned. Factors such as these make it necessary to develop a method robust to various environments, situations and occlusions. This paper proposes a new simple appearance model, with only one parameter, which is robust to prolonged partial occlusions and drastic appearance changes. The proposed strategy is based on covariance descriptors (which represent the tracked object) and an on-line nearest neighbor classifier (to track the object in the sequence). The proposed method performs exceptionally well and reduces the average error (in pixels) by 47% compared with tracking methods based on on-line boosting.

[1]  Shai Avidan,et al.  Ensemble Tracking , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Jorge Batista,et al.  A region covariance embedded in a particle filter for multi-objects tracking , 2008 .

[3]  Luc Van Gool,et al.  Beyond semi-supervised tracking: Tracking should be as simple as detection, but not simpler than recognition , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[4]  Horst Bischof,et al.  Semi-supervised On-Line Boosting for Robust Tracking , 2008, ECCV.

[5]  Ehud Rivlin,et al.  Robust Fragments-based Tracking using the Integral Histogram , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

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

[7]  Yangsheng Xu,et al.  Region covariance based probabilistic tracking , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[8]  Alvaro Soto,et al.  Human detection using a mobile platform and novel features derived from a visual saliency mechanism , 2010, Image Vis. Comput..

[9]  Nicholas Ayache,et al.  Geometric Means in a Novel Vector Space Structure on Symmetric Positive-Definite Matrices , 2007, SIAM J. Matrix Anal. Appl..

[10]  Hanqing Lu,et al.  Real-time visual tracking via Incremental Covariance Tensor Learning , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[11]  Michael Isard,et al.  BraMBLe: a Bayesian multiple-blob tracker , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[12]  Ieee Xplore,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence Information for Authors , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Stanley T. Birchfield,et al.  Elliptical head tracking using intensity gradients and color histograms , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[14]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[15]  W. Rossmann.  Lie Groups: An Introduction through Linear Groups , 2002 .

[16]  Fatih Murat Porikli,et al.  Pedestrian Detection via Classification on Riemannian Manifolds , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Fatih Murat Porikli,et al.  Covariance Tracking using Model Update Based on Lie Algebra , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[18]  Fatih Murat Porikli,et al.  Region Covariance: A Fast Descriptor for Detection and Classification , 2006, ECCV.

[19]  Jian Yao,et al.  Fast human detection from videos using covariance features , 2008, ECCV 2008.

[20]  Ming-Hsuan Yang,et al.  Visual tracking with online Multiple Instance Learning , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Eli Shechtman,et al.  In defense of Nearest-Neighbor based image classification , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.