The Active Recovery of 3D Motion Trajectories and Their Use in Prediction
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Human motion analysis: a review
Computer Vision and Image Understanding
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
3D People Tracking with Gaussian Process Dynamical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Model-Based Hand Tracking Using a Hierarchical Bayesian Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Real-time hand tracking using a mean shift embedded particle filter
Pattern Recognition
Sequential Monte Carlo tracking by fusing multiple cues in video sequences
Image and Vision Computing
A Probabilistic Approach to Integrating Multiple Cues in Visual Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Closed-world tracking of multiple interacting targets for indoor-sports applications
Computer Vision and Image Understanding
A local-motion-based probabilistic model for visual tracking
Pattern Recognition
Multisensor-based human detection and tracking for mobile service robots
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Adaptive mean-shift tracking with auxiliary particles
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Articulated pose identification with sparse point features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Tracking Multiple Visual Targets via Particle-Based Belief Propagation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Audio–Visual Active Speaker Tracking in Cluttered Indoors Environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Tracking of Multiple Targets Using Online Learning for Reference Model Adaptation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust visual tracking with structured sparse representation appearance model
Pattern Recognition
Computers & Mathematics with Applications
Real-time visual tracking via online weighted multiple instance learning
Pattern Recognition
A multi-resolution approach for massively-parallel hardware-friendly optical flow estimation
Journal of Visual Communication and Image Representation
Hi-index | 0.00 |
We propose a new dynamic model which can be used within blob trackers to track the target's center of gravity. A strong point of the model is that it is designed to track a variety of motions which are usually encountered in applications such as pedestrian tracking, hand tracking, and sports. We call the dynamic model a two-stage dynamic model due to its particular structure, which is a composition of two models: a liberal model and a conservative model. The liberal model allows larger perturbations in the target's dynamics and is able to account for motions in between the random-walk dynamics and the nearly constant-velocity dynamics. On the other hand, the conservative model assumes smaller perturbations and is used to further constrain the liberal model to the target's current dynamics. We implement the two-stage dynamic model in a two-stage probabilistic tracker based on the particle filter and apply it to two separate examples of blob tracking: 1) tracking entire persons and 2) tracking of a person's hands. Experiments show that, in comparison to the widely used models, the proposed two-stage dynamic model allows tracking with smaller number of particles in the particle filter (e.g., 25 particles), while achieving smaller errors in the state estimation and a smaller failure rate. The results suggest that the improved performance comes from the model's ability to actively adapt to the target's motion during tracking.