Robust Online Object Tracking Based on Feature Grouping and 2DPCA

• Published in: Mathematical problems in engineering Vol. 2013; no. 2013; pp. 1 - 7
• Main Authors: Jiang, Ming-Xin, Li, Min, Zhang, Jun-Xing
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2013; Hindawi Limited
• Subjects: Algorithms; Computation; Drift estimation; Error reduction; Image reconstruction; Iterative algorithms; Machine learning; Mathematical models; Normal distribution; On-line systems; Online; Principal components analysis; Reconstruction; Regularization; Robustness; State-of-the-art reviews; Tracking; Two dimensional analysis
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2013/352634

We present an online object tracking algorithm based on feature grouping and two-dimensional principal component analysis (2DPCA). Firstly, we introduce regularization into the 2DPCA reconstruction and develop an iterative algorithm to represent an object by 2DPCA bases. Secondly, the object templates are grouped into a more discriminative image and a less discriminative image by computing the variance of the pixels in multiple frames. Then, the projection matrix is learned according to the more discriminative image and the less discriminative image, and the samples are projected. The object tracking results are obtained using Bayesian maximum a posteriori probability estimation. Finally, we employ a template update strategy which combines incremental subspace learning and the error matrix to reduce tracking drift. Compared with other popular methods, our method reduces the computational complexity and is very robust to abnormal changes. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking algorithm achieves more favorable performance than several state-of-the-art methods.