This Article 
 Bibliographic References 
 Add to: 
Robust Visual Tracking Using Local Sparse Appearance Model and K-Selection
Dec. 2013 (vol. 35 no. 12)
pp. 2968-2981
Baiyang Liu, Dept. of Comput. Sci., Rutgers, State Univ. of New Jersey, Piscataway, NJ, USA
Junzhou Huang, Dept. of Comput. Sci. & Eng., Univ. of Arlington, Arlington, TX, USA
Casimir Kulikowski, Dept. of Comput. Sci., Rutgers, State Univ. of New Jersey, Piscataway, NJ, USA
Lin Yang, Dept. of Biostat., Univ. of Kentucky, Lexington, KY, USA
Online learned tracking is widely used for its adaptive ability to handle appearance changes. However, it introduces potential drifting problems due to the accumulation of errors during the self-updating, especially for occluded scenarios. The recent literature demonstrates that appropriate combinations of trackers can help balance the stability and flexibility requirements. We have developed a robust tracking algorithm using a local sparse appearance model (SPT) and K-Selection. A static sparse dictionary and a dynamically updated online dictionary basis distribution are used to model the target appearance. A novel sparse representation-based voting map and a sparse constraint regularized mean shift are proposed to track the object robustly. Besides these contributions, we also introduce a new selection-based dictionary learning algorithm with a locally constrained sparse representation, called K-Selection. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than alternatives reported in the recent literature.
Index Terms:
object tracking,image representation,learning (artificial intelligence),selection-based dictionary learning algorithm,robust visual tracking,k-selection,online learned tracking,drifting problems,occluded scenarios,flexibility requirements,stability requirements,robust tracking algorithm,local sparse appearance model,SPT,static sparse dictionary,dynamically updated online dictionary basis distribution,sparse representation-based voting map,sparse constraint regularized mean shift,Visualization,Target tracking,Histograms,Adaptation models,Heuristic algorithms,Encoding,dictionary learning,Sparse representation,tracking,K-selection,appearance model
Baiyang Liu, Junzhou Huang, Casimir Kulikowski, Lin Yang, "Robust Visual Tracking Using Local Sparse Appearance Model and K-Selection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 12, pp. 2968-2981, Dec. 2013, doi:10.1109/TPAMI.2012.215
Usage of this product signifies your acceptance of the Terms of Use.