Object tracking using incremental Fisher discriminant analysis

Ruei Sung Lin, Ming Hsuan Yang, Stephen E. Levinson

Research output: Contribution to journalConference articlepeer-review


This paper presents a novel object tracking algorithm using incremental Fisher Linear Discriminant (FLD) algorithm. The sample distribution of the target class is modeled by a single Gaussian and the non-target background class is modeled by a mixture of Gaussians. To a facilitate a multiclass classification problem, we recast the classic FLD algorithm in which the number of classes does not need to be pre-determined. The most discriminant projection matrix that best separates the samples in the projected space is computed using FLD at each frame. Based on the current target location, an efficient sampling algorithm is used to predict the possible locations in the next frame. Using the current projection matrix computed by FLD, the most likely candidate which is closed to the center of the target class in the projected space is selected. Since the FLD is repeatedly computed at each frame, we develop an incremental and efficient method to compute the projection matrix based on the previous results. Experimental results show that our tracker is able to follow the target with large lighting, pose and expression variation.

Original languageEnglish (US)
Pages (from-to)757-760
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
StatePublished - 2004
EventProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom
Duration: Aug 23 2004Aug 26 2004

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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