Financial Document Image Coding with Regions of Interest Using JPEG2000
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Fast linear discriminant analysis using binary bases
Pattern Recognition Letters
Fast algorithm for updating the discriminant vectors of dual-space LDA
IEEE Transactions on Information Forensics and Security
Proximal support vector machine using local information
Neurocomputing
Robust visual tracking via incremental maximum margin criterion
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
An incremental linear discriminant analysis using fixed point method
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
A survey of appearance models in visual object tracking
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
| Hi-index | 0.01 |
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.