Modeling a dynamic environment using a Bayesian multiple hypothesis approach
Artificial Intelligence
Probabilistic Data Association Methods for Tracking Complex Visual Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maintaining Multi-Modality through Mixture Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Multitarget Tracking with Split and Merged Measurements
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Decentralized Multiple Target Tracking Using Netted Collaborative Autonomous Trackers
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time Interactively Distributed Multi-Object Tracking Using a Magnetic-Inertia Potential Model
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Incremental Online Learning in High Dimensions
Neural Computation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Regressing Local to Global Shape Properties for Online Segmentation and Tracking
International Journal of Computer Vision
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This paper proposes a novel particle filtering framework for multi-target tracking by using online learned class-specific and instancespecific cues, called Data-Driven Particle Filtering (DDPF). The learned cues include an online learned geometrical model for excluding detection outliers that violate geometrical constraints, global pose estimators shared by all targets for particle refinement, and online Boosting based appearance models which select discriminative features to distinguish different individuals. Targets are clustered into two categories. Separatedtarget is tracked by an ISPF (incremental self-tuning particle filtering) tracker, in which particles are incrementally drawn and tuned to their best states by a learned global pose estimator; target-group is tracked by a joint-state particle filtering method in which occlusion reasoning is conducted. Experimental results on challenging datasets show the effectiveness and efficiency of the proposed method.