The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Extraction and Clustering of Motion Trajectories in Video
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Probabilistic curve-aligned clustering and prediction with regression mixture models
Probabilistic curve-aligned clustering and prediction with regression mixture models
Motion Segmentation by EM Clustering of Good Features
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 11 - Volume 11
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Motion Segmentation from Feature Trajectories with Missing Data
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
CamShift guided particle filter for visual tracking
Pattern Recognition Letters
Object tracking using SIFT features and mean shift
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Differential Earth Mover's Distance with Its Applications to Visual Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhanced Local Subspace Affinity for feature-based motion segmentation
Pattern Recognition
Trajectory-based representation of human actions
ICMI'06/IJCAI'07 Proceedings of the ICMI 2006 and IJCAI 2007 international conference on Artifical intelligence for human computing
Motion segmentation using the hadamard product and spectral clustering
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Visual tracking using the Earth Mover's Distance between Gaussian mixtures and Kalman filtering
Image and Vision Computing
Discovering clusters in motion time-series data
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A novel trajectory clustering approach for motion segmentation
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Real-Time Motion Segmentation of Sparse Feature Points at Any Speed
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A novel framework for motion segmentation and tracking by clustering incomplete trajectories
Computer Vision and Image Understanding
User-assisted sparse stereo-video segmentation
Proceedings of the 10th European Conference on Visual Media Production
Matching mixtures of curves for human action recognition
Computer Vision and Image Understanding
| Hi-index | 0.01 |
In this paper, we present a framework for visual object tracking based on clustering trajectories of image key points extracted from a video. The main contribution of our method is that the trajectories are automatically extracted from the video sequence and they are provided directly to a model-based clustering approach. In most other methodologies, the latter constitutes a difficult part since the resulting feature trajectories have a short duration, as the key points disappear and reappear due to occlusion, illumination, viewpoint changes and noise. We present here a sparse, translation invariant regression mixture model for clustering trajectories of variable length. The overall scheme is converted into a Maximum A Posteriori approach, where the Expectation-Maximization (EM) algorithm is used for estimating the model parameters. The proposed method detects the different objects in the input image sequence by assigning each trajectory to a cluster, and simultaneously provides the motion of all objects. Numerical results demonstrate the ability of the proposed method to offer more accurate and robust solution in comparison with the mean shift tracker, especially in cases of occlusions.