IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Tracking Using Foreground-Background Texture Discrimination
International Journal of Computer Vision
ACM Computing Surveys (CSUR)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Color in image and video processing: most recent trends and future research directions
Journal on Image and Video Processing - Color in Image and Video Processing
Performance evaluation of local colour invariants
Computer Vision and Image Understanding
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Object tracking using SIFT features and mean shift
Computer Vision and Image Understanding
Real-time bag of words, approximately
Proceedings of the ACM International Conference on Image and Video Retrieval
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Thirteen Hard Cases in Visual Tracking
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Fast occluded object tracking by a robust appearance filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating Color and Shape-Texture Features for Adaptive Real-Time Object Tracking
IEEE Transactions on Image Processing
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Tracking can be seen as an online learning problem, where the focus is on discriminating object from background. From this point of view, features play a key role as the tracking accuracy depends on how well the feature distinguishes object and background. Current discriminative trackers use traditional features such as intensity, RGB and full body shape features. In this paper, we propose to use color invariant SURF features in the discriminative tracking. This set of invariant features has been shown to be of increased invariance and discriminative power. The resulting tracker inherits a good discrimination between object and background while keeping advantages of the discriminative tracking framework. Experiments on a dataset of 80 videos covering a wide range of tracking circumstances show that the tracker is robust to changes in object appearance, lighting conditions and able to track objects under cluttered scenes and partial occlusion.