Particle Filter Based Object Tracking with Discriminative Feature Extraction and Fusion
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Efficient content analysis engine for visual surveillance network
IEEE Transactions on Circuits and Systems for Video Technology
A hierarchical feature fusion framework for adaptive visual tracking
Image and Vision Computing
Particle Filtering with Region-based Matching for Tracking of Partially Occluded and Scaled Targets
SIAM Journal on Imaging Sciences
Feature prominence-based weighting scheme for video tracking
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Hi-index | 0.00 |
In this paper, we propose a tracking algorithm based on an adaptive multifeature statistical target model. The features are combined in a single particle filter by weighting their contributions using a novel reliability measure derived from the particle distribution in the state space. This measure estimates the reliability of the information by measuring the spatial uncertainty of features. A modified resampling strategy is also devised to account for the needs of the feature reliability estimation. We demonstrate the algorithm using color and orientation features. Color is described with partwise normalized histograms. Orientation is described with histograms of the gradient directions that represent the shape and the internal edges of a target. A feedback from the state estimation is used to align the orientation histograms as well as to adapt the scales of the filters to compute the gradient. Experimental results over a set of real-world sequences show that the proposed feature weighting procedure outperforms state-of-the-art solutions and that the proposed adaptive multifeature tracker improves the reliability of the target estimate while eliminating the need of manually selecting each feature's relevance.