On the Detection of Motion and the Computation of Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-based object tracking in monocular image sequences of road traffic scenes
International Journal of Computer Vision
Computing occluding and transparent motions
International Journal of Computer Vision
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determination of Optical Flow and its Discontinuities using Non-Linear Diffusion
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixed-State Auto-Models and Motion Texture Modeling
Journal of Mathematical Imaging and Vision
High-Speed Human Motion Recognition Based on a Motion History Image and an Eigenspace
IEICE - Transactions on Information and Systems
Shadow identification and classification using invariant color models
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Time delay estimation with unknown spatially correlated Gaussiannoise
IEEE Transactions on Signal Processing
Motion estimation using higher order statistics
IEEE Transactions on Image Processing
HOS-based image sequence noise removal
IEEE Transactions on Image Processing
Image motion estimation algorithms using cumulants
IEEE Transactions on Image Processing
Human action recognition using ordinal measure of accumulated motion
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
Real time illumination invariant motion change detection
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
Real time motion changes for new event detection and recognition
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Activity detection and recognition of daily living events
Proceedings of the 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare
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A robust, theoretically founded approach for the extraction of temporal templates corresponding to areas of motion in video, is presented. Higher order statistics (kurtosis) are employed to extract activity areas, i.e., binary masks indicating which pixels in a video are active. The application of the kurtosis on illumination changes modeled as Gaussians and mixture of Gaussians is shown to be sensitive to outliers for both models, thus correctly localizing active pixels. Activity areas are compared to existing, difference-based temporal templates, known as motion energy images, and the robustness of both categories of temporal templates to additive noise is analyzed theoretically. Experiments with numerous real videos with additive noise, both indoors and outdoors, are conducted to compare the robustness of the activity areas and motion energy images, and their temporal extensions, the activity history areas, and motion history images. As expected from the theoretical analysis, the kurtosis-based activity areas prove to be more robust than the difference-based templates. Challenging videos containing occlusions, varying backgrounds, and shadows are also examined, and it is shown that the proposed approach outperforms the difference-based method for these cases, as well, consistently providing reliable localization of activity under a wide range of difficult circumstances. The proposed approach provides good results at a very low computational cost, and without requiring prior knowledge about the scene, nor training of any kind.