Tracking and data association
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Democratic Integration: Self-Organized Integration of Adaptive Cues
Neural Computation
ACM Computing Surveys (CSUR)
Localization of multiple emitters based on the sequential PHD filter
Signal Processing
Statistical Learning and Pattern Analysis for Image and Video Processing
Statistical Learning and Pattern Analysis for Image and Video Processing
Detection-guided multi-target Bayesian filter
Signal Processing
The Gaussian Mixture Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing
Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing - Part II
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
A Consistent Metric for Performance Evaluation of Multi-Object Filters
IEEE Transactions on Signal Processing - Part I
Adaptive Multifeature Tracking in a Particle Filtering Framework
IEEE Transactions on Circuits and Systems for Video Technology
Data-Driven Probability Hypothesis Density Filter for Visual Tracking
IEEE Transactions on Circuits and Systems for Video Technology
Efficient Multitarget Visual Tracking Using Random Finite Sets
IEEE Transactions on Circuits and Systems for Video Technology
Towards robust multi-cue integration for visual tracking
Machine Vision and Applications
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Probability hypothesis density (PHD) based trackers have enjoyed growing popularity in recent years, particularly in the field of nonlinear non-Gaussian visual tracking scenarios. These visual trackers can be degraded by a variety of factors, including changes of illumination, occlusion, poor image contrast, shape and appearance variation, clutter and other unmodeled changes of tracked objects. In this paper, for enhancing the performance of PHD based trackers, both scale invariant feature and color distribution feature are used as descriptors of targets of interest. To adaptively adjust the weights of each feature in the tracking process, a confidence measure, i.e., a quantitative measure for the spatial uncertainty of each feature is incorporated into the multifeature tracking algorithm. Experimental results show that the proposed multifeature tracker can improve the reliability and robustness of state estimation and the number estimation in tracking a variable number of objects of varying scales even when background region was similar to the object's appearance.