Efficient visual object tracking with online nearest neighbor classifier


Journal Article

A tracking-by-detection framework is proposed that combines nearest-neighbor classification of bags of features, efficient subwindow search, and a novel feature selection and pruning method to achieve stability and plasticity in tracking targets of changing appearance. Experiments show that near-frame-rate performance is achieved (sans feature detection), and that the state of the art is improved in terms of handling occlusions, clutter, changes of scale, and of appearance. A theoretical analysis shows why nearest neighbor works better than more sophisticated classifiers in the context of tracking. © 2011 Springer-Verlag Berlin Heidelberg.

Full Text

Duke Authors

Cited Authors

  • Gu, S; Zheng, Y; Tomasi, C

Published Date

  • March 16, 2011

Published In

Volume / Issue

  • 6492 LNCS / PART 1

Start / End Page

  • 271 - 282

Electronic International Standard Serial Number (EISSN)

  • 1611-3349

International Standard Serial Number (ISSN)

  • 0302-9743

Digital Object Identifier (DOI)

  • 10.1007/978-3-642-19315-6_21

Citation Source

  • Scopus