Robust tracking using local sparse appearance model and K-selection
- 1 June 2011
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1313-1320
- https://doi.org/10.1109/cvpr.2011.5995730
Abstract
Online learned tracking is widely used for it's 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 stability and flexibility requirements. We have developed a robust tracking algorithm using a local sparse appearance model (SPT). A static sparse dictionary and a dynamically online updated basis distribution model the target appearance. A novel sparse representation-based voting map and sparse constraint regularized mean-shift support the robust object tracking. Besides these contributions, we also introduce a new 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.Keywords
This publication has 20 references indexed in Scilit:
- PROST: Parallel robust online simple trackingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010
- Online dictionary learning for sparse codingPublished by Association for Computing Machinery (ACM) ,2009
- Discriminatively trained particle filters for complex multi-object trackingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Learning to track with multiple observersPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Signal Recovery From Random Measurements Via Orthogonal Matching PursuitIEEE Transactions on Information Theory, 2007
- Incremental Learning for Robust Visual TrackingInternational Journal of Computer Vision, 2007
- Covariance Tracking using Model Update Based on Lie AlgebraPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- A probabilistic framework for combining tracking algorithmsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Active Appearance Models RevisitedInternational Journal of Computer Vision, 2004
- The template update problemIEEE Transactions on Pattern Analysis and Machine Intelligence, 2004