On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean shift blob tracking with kernel histogram filtering and hypothesis testing
Pattern Recognition Letters
Motion-Alert: automatic anomaly detection in massive moving objects
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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A basic requirement for a practical tracking system is to adjust the tracking model in real time when the appearance of the tracked object changes. However, since the scale of the targets often varied irregularly, systems with fixed-size tracking window usually could not accommodate to these scenarios. In present paper, a new multi-scale information measure for image was introduced to probe the size-changes of tracked objects. An automatic window-size updating method was then proposed and integrated into the classical color histogram based meanshift and particle filtering tracking frameworks. Experimental results demonstrated that the improved algorithms could select the proper size of tracking window not only when the object scale increases but the scale decreases as well with minor extra computational overhead.