CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
ACM Computing Surveys (CSUR)
Real-time hand tracking using a mean shift embedded particle filter
Pattern Recognition
Tracking and recognizing actions of multiple hockey players using the boosted particle filter
Image and Vision Computing
Structural similarity-based object tracking in multimodality surveillance videos
Machine Vision and Applications
Robust visual tracking for multiple targets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
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In this paper, the ways of optimising a Particle Filter video tracking algorithm are investigated. The optimisation scheme discussed in this work is based on hybridising a Particle Filter tracker with a deterministic mode search technique applied to the particle distribution. Within this scheme, an extension of the recently introduced structural similarity tracker is proposed and compared with the approach based on separate and combined colour and mean-shift tracker. The new approach is especially applicable to real-world video surveillance scenarios, in which the presence of multiple targets and complex background pose a non-trivial challenge to automated trackers. The preliminary results indicate that a considerable improvement in tracking is achieved by applying the optimisation scheme, at the price of a moderate computational complexity increase of the algorithm.