CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Multiple Object Tracking via a Hierarchical Particle Filter
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Robust Fragments-based Tracking using the Integral Histogram
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Online Tracking and Reacquisition Using Co-trained Generative and Discriminative Trackers
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We propose a robust tracker based on tracking, learning and detection to follow an object in a long term. Our tracker consists of three different parts: a short term tracker, a detector, and an online object model. For the shortterm tracker, we employ the Lucas Kanade tracker to keep following the object frame by frame. Meanwhile, the sequential randomized forest using a 5bit Haarlike Binary Pattern feature plays as a detector to detect all possible object candidates in the current frame. The online template-based object model consisting of positive and negative image patches decides which the best target is. Our method is consistent against challenges such as viewpoint changes, various lighting conditions, and cluttered background. Moreover, our method is efficiently able to reacquire the object efficiently even after it's out of view or in total occlusion. We also propose an efficient way to extend our tracker for multiple faces tracking application. Extensive experiments are provided to show the robust of our tracker. Comparisons with other state-of-the-art trackers are also demonstrated.