CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Robust Obstacle Detection from Stereoscopic Image Sequences Using Kalman Filtering
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Moveable interactive projected displays using projector based tracking
Proceedings of the 18th annual ACM symposium on User interface software and technology
Tracking of the Articulated Upper Body on Multi-View Stereo Image Sequences
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Real-Time Camera Tracking Using Known 3D Models and a Particle Filter
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Tracking 3D Human Body using Particle Filter in Moving Monocular Camera
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
ACM Computing Surveys (CSUR)
Video object tracking using adaptive Kalman filter
Journal of Visual Communication and Image Representation
Real-time hand tracking using a mean shift embedded particle filter
Pattern Recognition
Smart particle filtering for high-dimensional tracking
Computer Vision and Image Understanding
Tracking human motion using auxiliary particle filters and iterated likelihood weighting
Image and Vision Computing
A Quantitative Evaluation of Video-based 3D Person Tracking
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Research on 3D Hand Tracking Using Particle Filtering
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 07
Face detection and tracking using a Boosted Adaptive Particle Filter
Journal of Visual Communication and Image Representation
Object tracking using SIFT features and mean shift
Computer Vision and Image Understanding
Online updating appearance generative mixture model for meanshift tracking
Machine Vision and Applications
Object Tracking Algorithm Based on Meanshift Algorithm Combining with Motion Vector Analysis
ETCS '09 Proceedings of the 2009 First International Workshop on Education Technology and Computer Science - Volume 01
Sequential particle generation for visual tracking
IEEE Transactions on Circuits and Systems for Video Technology
Computer Vision and Image Understanding
Exploiting motion correlations in 3-D articulated human motion tracking
IEEE Transactions on Image Processing
Tracking Motion, Deformation, and Texture Using Conditionally Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Study on Smoothing for Particle-Filtered 3D Human Body Tracking
International Journal of Computer Vision
Real-Time Vehicle Tracking by Kalman Filtering and Gabor Decomposition
ICISE '09 Proceedings of the 2009 First IEEE International Conference on Information Science and Engineering
Tracking objects with generic calibrated sensors: An algorithm based on color and 3D shape features
Robotics and Autonomous Systems
Particle filtering strategies for data fusion dedicated to visual tracking from a mobile robot
Machine Vision and Applications
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
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Shape Features of Overlapping Boundary for Classification of Moving Vehicles
International Journal of Computer Vision and Image Processing
Hi-index | 0.01 |
In the recent years, the 3D visual research has gained momentum with publications appearing for all aspects of 3D including visual tracking. This paper presents a review of the literature published for 3D visual tracking over the past five years. The work particularly focuses on stochastic filtering techniques such as particle filter and Kalman filter. These two filters are extensively used for tracking due to their ability to consider uncertainties in the estimation. The improvement in computational power of computers and increasing interest in robust tracking algorithms lead to increase in the use of stochastic filters in visual tracking in general and 3D visual tracking in particular. Stochastic filters are used for numerous applications in the literature such as robot navigation, computer games and behavior analysis. Kalman filter is a linear estimator which approximates system's dynamics with Gaussian model while particle filter approximates system's dynamics using weighted samples. In this paper, we investigate the implementation of Kalman and particle filters in the published work and we provide comparison between these techniques qualitatively as well as quantitatively. The quantitative analysis is in terms of computational time and accuracy. The quantitative analysis has been implemented using four parameters of the tracked object which are object position, velocity, size of bounding ellipse and orientation angle.