Color-Based Tracking of Heads and Other Mobile Objects at Video Frame Rates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Efficient Mean-Shift Tracking via a New Similarity Measure
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Object tracking using SIFT features and mean shift
Computer Vision and Image Understanding
Mean shift blob tracking with kernel histogram filtering and hypothesis testing
Pattern Recognition Letters
Real time hand tracking by combining particle filtering and mean shift
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Learning common behaviors from large sets of unlabeled temporal series
Image and Vision Computing
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This paper proposes a tracking architecture that finds a trade-off between accuracy and efficiency, via a combined solution of motion and appearance information. We explore the use of color features into a tracking pipeline based on Kalman filtering. The devised architecture is made of simple modules, combined to reach a robust final result, while keeping the computation cost low (we perform $20$ fps). The method has been evaluated on three benchmark datasets and is currently under use on real video-surveillance systems, reporting very good tracking results.