Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Statistical Background Subtraction for a Mobile Observer
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Background Modeling and Subtraction of Dynamic Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An Invitation to 3-D Vision: From Images to Geometric Models
An Invitation to 3-D Vision: From Images to Geometric Models
Motion Layer Extraction in the Presence of Occlusion Using Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Level Grouping for Video Shots
International Journal of Computer Vision
Automated Alignment of Robotic Pan-Tilt Camera Units Using Vision
International Journal of Computer Vision
Video object segmentation by motion-based sequential feature clustering
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Image alignment and stitching: a tutorial
Foundations and Trends® in Computer Graphics and Vision
International Journal of Computer Vision
Inertial-aided KLT feature tracking for a moving camera
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Motion-based background subtraction using adaptive kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Motion Segmentation by Learning Homography Matrices from Motor Signals
CRV '11 Proceedings of the 2011 Canadian Conference on Computer and Robot Vision
Key-segments for video object segmentation
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Moving object segmentation from an image sequence is essential for a robot to interact with its environment. Traditional vision approaches appeal to pure motion analysis on videos without exploiting the source of the background motion. We observe, however, that the background motion (from the robot's egocentric view) has stronger correlation to the robot's motor signals than the foreground motion. We propose a novel approach to detecting moving objects by clustering features into background and foreground according to their motion consistency with motor signals. Specifically, our approach learns homography and fundamental matrices as functions of motor signals, and predict sparse feature locations from the learned matrices. The errors between the predictions and their actual tracked locations are used to label them into background and foreground. The labels of the sparse features are then propagated to all pixels. Our approach does not require building a dense mosaic background or searching for affine, homography, or fundamental matrix parameters for foreground separation. In addition, it does not need to explicitly model the intrinsic and extrinsic calibration parameters hence requires much less prior geometry knowledge. It works completely in 2D image space, and does not involve any complex analysis or computation in 3D space.