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
Counting Crowded Moving Objects
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A spectral clustering approach to motion segmentation based on motion trajectory
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
A Coarse-to-Fine Strategy for Vehicle Motion Trajectory Clustering
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Segmenting Moving Objects in MPEG Videos in the Presence of Camera Motion
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Segmenting, modeling, and matching video clips containing multiple moving objects
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning semantic scene models by trajectory analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Real-Time Motion Trajectory-Based Indexing and Retrieval of Video Sequences
IEEE Transactions on Multimedia
Models for motion-based video indexing and retrieval
IEEE Transactions on Image Processing
Robust segmentation and tracking of colored objects in video
IEEE Transactions on Circuits and Systems for Video Technology
Motion segmentation by model-based clustering of incomplete trajectories
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
A novel framework for motion segmentation and tracking by clustering incomplete trajectories
Computer Vision and Image Understanding
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We propose a novel clustering scheme for spatio-temporal segmentation of sparse motion fields obtained from feature tracking. The approach allows for the segmentation of meaningful motion components in a scene, such as short- and long-term motion of single objects, groups of objects and camera motion. The method has been developed within a project on the analysis of low-quality archive films. We qualitatively and quantitatively evaluate the performance and the robustness of the approach. Results show, that our method successfully segments the motion components even in particularly noisy sequences.