Elements of information theory
Elements of information theory
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Handbook of Computer Vision and Applications: Volume 2: From Images to Features
Handbook of Computer Vision and Applications: Volume 2: From Images to Features
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real Time Face and Object Tracking as a Component of a Perceptual User Interface
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time hand tracking using a mean shift embedded particle filter
Pattern Recognition
Robust visual tracking for multiple targets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Adaptive mean-shift tracking with auxiliary particles
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We present a new approach towards efficient and robust tracking by incorporating the efficiency of the mean shift algorithm with the robustness of the particle filtering. The mean shift tracking algorithm is robust and effective when the representation of a target is sufficiently discriminative, the target does not jump beyond the bandwidth, and no serious distractions exist. In case of sudden motion, the particle filtering outperforms the mean shift algorithm at the expense of using a large particle set. In our approach, the mean shift algorithm is used as long as it provides reasonable performance. Auxiliary particles are introduced to conquer the distraction and sudden motion problems when such threats are detected. Moreover, discriminative features are selected according to the separation of the foreground and background distributions. We demonstrate the performance of our approach by comparing it with other trackers on challenging image sequences.