Performance of optical flow techniques
International Journal of Computer Vision
Realistic, hardware-accelerated shading and lighting
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistics and Data Analysis in Geology
Statistics and Data Analysis in Geology
Robust analysis of feature spaces: color image segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
An Efficient Technique for Segmentation of Key Object(s) from Video Shots
ITCC '03 Proceedings of the International Conference on Information Technology: Computers and Communications
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Behavioral Priors for Detection and Tracking of Pedestrians in Video Sequences
International Journal of Computer Vision
Monocular model-based 3D tracking of rigid objects
Foundations and Trends® in Computer Graphics and Vision
Object tracking in clutter and partial occlusion through rule-driven utilization of Snakes
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Object segmentation using graph cuts based active contours
Computer Vision and Image Understanding
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
Motion segmentation by multistage affine classification
IEEE Transactions on Image Processing
Combined key-frame extraction and object-based video segmentation
IEEE Transactions on Circuits and Systems for Video Technology
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Multimedia analysis usually deals with a large amount of video data with a significant number of moving objects. Often it is necessary to reduce the amount of data and to represent the video in terms of moving objects and events. Event analysis can be built on the detection of moving objects. In order to automatically process a variety of video content in different domain, largely unsupervised moving object segmentation algorithms are needed. We propose a fully unsupervised system for moving object segmentation that does not require any restriction on the video content. Our approach to extract moving objects relies on a mesh-based combination of results from colour segmentation (Mean Shift) and motion segmentation by feature point tracking (KLT tracker). The proposed algorithm has been evaluated using precision and recall measures for comparing moving objects and their colour segmented regions with manually labelled ground truth data. Results show that the algorithm is comparable to other state-of-the-art algorithms. The extracted information is used in a search and retrieval tool. For that purpose a moving object representation in MPEG-7 is implemented. It facilitates high performance indexing and retrieval of moving objects and events in large video databases, such as the search for similar moving objects occurring in a certain period.