International Journal of Computer Vision
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
TurboPixels: Fast Superpixels Using Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Superpixels and supervoxels in an energy optimization framework
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Robust Object Tracking with Online Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Hi-index | 0.00 |
In this work, we propose a tracking algorithm that robustly handles complex variations in target appearance, scale, occlusion, and background. In particular, the algorithm exploits a novel superpixel-based appearance model for visual tracking. From the initial tracking window, we extract superpixels and compute their histogram features. In subsequent frames, we search for the region that maximizes the similarity of the superpixel features. Our algorithm detects target occlusion and updates the appearance model accordingly. As well, the model is updated to handle large-scale variations. We present experimental results on several publicly available challenging sequences. Qualitative and quantitative evaluation of our tracking algorithm show improved performance over state-of-the-art trackers.