Real-time and accurate segmentation of moving objects in dynamic scene
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
Real-time spatiotemporal segmentation of video objects in the H.264 compressed domain
Journal of Visual Communication and Image Representation
EURASIP Journal on Applied Signal Processing
Spatiotemporal region enhancement and merging for unsupervized object segmentation
Journal on Image and Video Processing
Taxonomy of directing semantics for film shot classification
IEEE Transactions on Circuits and Systems for Video Technology
Video segmentation based on motion coherence of particles in a video sequence
IEEE Transactions on Image Processing
Reconfigurable Morphological Image Processing Accelerator for Video Object Segmentation
Journal of Signal Processing Systems
Combining hausdorff distance, HSV histogram and nonextensive entropy for object tracking
MUSP'06 Proceedings of the 6th WSEAS international conference on Multimedia systems & signal processing
Graph-Based spatio-temporal region extraction
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
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Rapid developments in the Internet and multimedia applications allow us to access large amounts of image and video data. While significant progress has been made in digital data compression, content-based functionalities are still quite limited. Many existing techniques in content-based retrieval are based on global visual features extracted from the entire image. In order to provide more efficient content-based functionalities for video applications, it is necessary to extract meaningful video objects from scenes to enable object-based representation of video content. Object-based representation is also introduced by MPEG-4 to enable content-based functionality and high coding efficiency. In this paper, we propose a new algorithm that automatically extracts meaningful video objects from video sequences. The algorithm begins with the robust motion segmentation on the first two successive frames. To detect moving objects, segmented regions are grouped together according to their spatial similarity. A binary object model for each moving object is automatically derived and tracked in subsequent frames using the generalized Hausdorff distance. The object model is updated for each frame to accommodate for complex motions and shape changes of the object. Experimental results using different types of video sequences are presented to demonstrate the efficiency and accuracy of our proposed algorithm.