Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Object Tracking Using Naive Bayesian Classifiers
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Improved object tracking using an adaptive colour model
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
QP_TR trust region blob tracking through scale-space with automatic selection of features
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Blob tracking with adaptive feature selection and accurate scale determination
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Characterization and synthesis of objects using growing neural gas
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
A graph-based feature combination approach to object tracking
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
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There are many studies that use color space models (CSM) for detection of faces in an image. Most a priori select a given CSM, and proceed to use the selected model for color segmentation of the face by constructing a color distribution model (CDM). There is limited work on finding the overall best CSM. Those that do only compare a few of the models. These techniques have been applied to face tracking procedures in environments with limited illumination changes. We develop a procedure to adaptively change the CSM throughout the processing of a video. We show that this works in environments where the face moves through multi-positioned light sources with varying types of illumination such as; colored, fluorescent, ambient, and reflected light. A test of the procedure using the 2D color space models; RG, rg, HS, YQ and CbCr found that switching between the RG and HS color spaces resulted in increased tracking performance. In addition, we have proposed a new performance measure for evaluating color-tracking algorithms, which include both accuracy and robustness of the tracking window. The methodology developed can be used to find the optimal (CSM,CDM) combination in adaptive color tracking systems.