Robust Clustering with Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of optical flow techniques
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Robust Tracking of Position and Velocity With Kalman Snakes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Digital Picture Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Selection of Discriminative Tracking Features
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Unifying Approach to Hard and Probabilistic Clustering
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Robust Visual Tracking via Pixel Classification and Integration
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper, we brought out a noise-insensitive pixel-wise object tracking algorithm whose kernel is a new reliable data grouping algorithm that introduces the reliability evaluation into the existing K-means clustering (called as RK-means clustering). The RK-means clustering concentrates on two problems of the existing K-mean clustering algorithm: 1) the unreliable clustering result when the noise data exists; 2) the bad/wrong clustering result caused by the incorrectly assumed number of clusters. The first problem is solved by evaluating the reliability of classifying an unknown data vector according to the triangular relationship among it and its two nearest cluster centers. Noise data will be ignored by being assigned low reliability. The second problem is solved by introducing a new group merging method that can delete pairs of "too near" data groups by checking their variance and average reliability, and then combining them together. We developed a video-rate object tracking system (called as RK-means tracker) with the proposed algorithm. The extensive experiments of tracking various objects in cluttered environments confirmed its effectiveness and advantages.