Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
ACM Computing Surveys (CSUR)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Performance evaluation metrics and statistics for positional tracker evaluation
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
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There are two key issues for the particle filtering based object tracking: the proposed distribution p(Xit|Xiiit-I) and the likelihood p(zt|Xit) between the prediction and the actual observation. The kernel color histogram based particle filter (CHPF) has achieved very good tracking performance with respect to partial occlusion, rotation and scale variations. However, it would easily lose an object when the object has similar appearance as the background or when the illumination changes. To address these problems, in this paper we introduce the Semi-Supervised On-line Boosting algorithm (SSOB) and connect the confidence of SSOB with the observation model of particle filter. This new method, Semi-Supervised Boosting On-line Object Tracking based Particle Filter (SBPF), can better distinguish objects from the background. It also has faster while more robust adaptation to the change of objects' appearance and the environment illumination condition. The core to the enhanced characteristics is implementing the likelihood as the Semi-Supervised On-line Boosting operator. Extensive experiments show the superior performance of this novel method under aforementioned difficult scenarios.