Robust regression and outlier detection
Robust regression and outlier detection
Motion Field and Optical Flow: Qualitative Properties
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion and Structure from Orthographic Projections
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust regression methods for computer vision: a review
International Journal of Computer Vision
Invariant Descriptors for 3D Object Recognition and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Detecting Moving Objects Using the Rigidity Constraint
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing 3-D objects using 2-D images
Recognizing 3-D objects using 2-D images
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Motion segmentation and qualitative dynamic scene analysis from an image sequence
International Journal of Computer Vision
Segmentation of moving objects by robust motion parameter estimation over multiple frames
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
Digital video processing
Artificial Intelligence - Special volume on computer vision
Matrix computations (3rd ed.)
Tracking Human Motion in Structured Environments Using a Distributed-Camera System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Independent Motion: The Statistics of Temporal Continuity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Independent Motion Detection in 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Theory of Reconstruction from Image Motion
Theory of Reconstruction from Image Motion
ASSET-2: Real-Time Motion Segmentation and Shape Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Obstacle Detection Based on Qualitative and Quantitative 3D Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Independent 3D Motion Detection Based on Depth Elimination in Normal Flow Fields
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
In defence of the 8-point algorithm
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A Unified Approach to Moving Object Detection in 2D and 3D Scenes
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Three-dimensional reconstruction under varying constraints on camera geometry for robotic navigation scenarios
What Can Projections of Flow Fields Tell Us About the Visual Motion
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Independent 3D Motion Detection Using Rsidual Parallax Normal Flow Fields
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Fast scene change detection using direct feature extraction fromMPEG compressed videos
IEEE Transactions on Multimedia
Early detection of independent motion from active control of normal image flow patterns
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ACM Transactions on Accessible Computing (TACCESS)
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This paper presents a novel solution to the problem frequently encountered in military/intelligence surveillance: automatic mining video data for independently moving targets. Instead of attempting to detect each individual independently moving target in each frame, this method focuses on determining whether or not each frame has independent motion. Consequently, it differs from the existing literature on this topic in the following two aspects: (1) fast detection (2) detection directly from the compressed video streams instead of from the image sequences. These two aspects are motivated from the applications that this research aims to address, and therefore, are the contributions to the literature of independent motion detection. The solution is based on the Linear System Consistency Analysis, and thus is called LSCA. A number of distinctive technical advantages of LSCA as compared with the existing methods in the literature are identified. Evaluations from both simulated and real data show that LSCA holds a great promise in achieving fast detection without sacrificing the reasonable detection accuracy.