On the representation and estimation of spatial uncertainly
International Journal of Robotics Research
Three-dimensional motion computation and object segmentation in a long sequence of stereo frames
International Journal of Computer Vision
CVGIP: Image Understanding
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
A review of statistical data association for motion correspondence
International Journal of Computer Vision
Region-based tracking using affine motion models in long image sequences
CVGIP: Image Understanding
Digital video processing
Artificial Intelligence - Special volume on computer vision
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region-based parametric motion segmentation using color information
Graphical Models and Image Processing
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Robust Tracking of Position and Velocity With Kalman Snakes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Kalman Filter Approach to Direct Depth Estimation Incorporating Surface Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Tracking Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion and Structure from Image Sequences
Motion and Structure from Image Sequences
Recursive Estimation of Motion, Structure, and Focal Length
IEEE Transactions on Pattern Analysis and Machine Intelligence
Occlusion Robust Tracking Utilizing Spatio-Temporal Markov Random Field Model
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Motion segmentation by multistage affine classification
IEEE Transactions on Image Processing
Statistical deformable model-based segmentation of image motion
IEEE Transactions on Image Processing
Tracking visible boundary of objects using occlusion adaptive motion snake
IEEE Transactions on Image Processing
Tracking a dynamic set of feature points
IEEE Transactions on Image Processing
A layered video object coding system using sprite and affine motion model
IEEE Transactions on Circuits and Systems for Video Technology
Video segmentation based on multiple features for interactive multimedia applications
IEEE Transactions on Circuits and Systems for Video Technology
Semiautomatic segmentation and tracking of semantic video objects
IEEE Transactions on Circuits and Systems for Video Technology
Semi-automatic video object segmentation in the presence of occlusion
IEEE Transactions on Circuits and Systems for Video Technology
Semiautomatic video object segmentation using VSnakes
IEEE Transactions on Circuits and Systems for Video Technology
Optimal recursive clustering of likelihood functions for multiple object tracking
Pattern Recognition Letters
Hi-index | 0.00 |
In this paper, we propose a new approach that uses a motion-estimation based framework for video tracking of objects in cluttered environments. Our approach is semi-automatic, in the sense that a human is called upon to delineate the boundary of the object to be tracked in the first frame of the image sequence. The approach presented requires no camera calibration; therefore it is not necessary that the camera be stationary. The heart of the approach lies in extracting features and estimating motion through multiple applications of Kalman filtering. The estimated motion is used to place constraints on where to seek feature correspondences; successful correspondences are subsequently used for Kalman-based recursive updating of the motion parameters. Associated with each feature is the frame number in which the feature makes its first appearance in an image sequence. All features that make first-time appearances in the same frame are grouped together for Kalman-based updating of motion parameters. Finally, in order to make the tracked object look visually familiar to the human observer, the system also makes its best attempt at extracting the boundary contour of the object--a difficult problem in its own right since self-occlusion created by any rotational motion of the tracked object would cause large sections of the boundary contour in the previous frame to disappear in the current frame. Boundary contour is estimated by projecting the previousframe contour into the current frame for the purpose of creating neighborhoods in which to search for the true boundary in the current frame. Our approach has been tested on a wide variety of video sequences, some of which are shown in this paper.