Trajectory Triangulation: 3D Reconstruction of Moving Points from a Monocular Image Sequence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Recovering 3-D shape and reflectance from a small number of photographs
EGRW '03 Proceedings of the 14th Eurographics workshop on Rendering
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Visual Modeling with a Hand-Held Camera
International Journal of Computer Vision
A Quasi-Dense Approach to Surface Reconstruction from Uncalibrated Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Layer Extraction in the Presence of Occlusion Using Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Feature-based Approach for Dense Segmentation and Estimation of Large Disparity Motion
International Journal of Computer Vision
3D Reconstruction of Background and Objects Moving on Ground Plane Viewed from a Moving Camera
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Extraction and temporal segmentation of multiple motion trajectories in human motion
Image and Vision Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face detection and tracking in a video by propagating detection probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion segmentation by multistage affine classification
IEEE Transactions on Image Processing
Spatiotemporal video segmentation based on graphical models
IEEE Transactions on Image Processing
Unsupervised video segmentation based on watersheds and temporal tracking
IEEE Transactions on Circuits and Systems for Video Technology
An integrated approach for content-based video object segmentation and retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Video object segmentation: a compressed domain approach
IEEE Transactions on Circuits and Systems for Video Technology
Robust segmentation and tracking of colored objects in video
IEEE Transactions on Circuits and Systems for Video Technology
Video object segmentation using Bayes-based temporal tracking and trajectory-based region merging
IEEE Transactions on Circuits and Systems for Video Technology
A Bayesian approach to video object segmentation via merging 3-D watershed volumes
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Modeling Background and Segmenting Moving Objects from Compressed Video
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we describe a video-object segmentation and 3D-trajectory estimation method for the analysis of dynamic scenes from a monocular uncalibrated view. Based on the color and motion information among video frames, our proposed method segments the scene, calibrates the camera, and calculates the 3D trajectories of moving objects. It can be employed for video-object segmentation, 2D-to-3D video conversion, video-object retrieval, etc. In our method, reliable 2D feature motions are established by comparing SIFT descriptors among successive frames, and image over-segmentation is achieved using a graph-based method. Then, the 2D motions and the segmentation result iteratively refine each other in a hierarchically structured framework to achieve video-object segmentation. Finally, the 3D trajectories of the segmented moving objects are estimated based on a local constant-velocity constraint, and are refined by a Hidden Markov Model (HMM)-based algorithm. Experiments show that the proposed framework can achieve a good performance in terms of both object segmentation and 3D-trajectory estimation.