Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Color Space Switching for Face Tracking in Multi-Colored Lighting Environments
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Real-Time Tracking Using Trust-Region Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We propose a novel color based tracking framework in which an object configuration and color feature are simultaneously determined via scale space filtration. The tracker can automatically select discriminative color feature that well distinguishes foreground from background. According to that feature, a likelihood image of the target is generated for each incoming frame. The target’s area turns into a blob in the likelihood image. The scale of this blob can be determined based on the local maximum of differential scale-space filters. We employ the QP_TR trust region algorithm to search for the local maximum of multi-scale normalized Laplacian filter of the likelihood image to locate the target as well as determine its scale. Based on the tracking results of sequence examples, the proposed method has been proven to be resilient to the color and lighting changes, be capable of describing the target more accurately and achieve much better tracking precision.