Robust multiple car tracking with occlusion reasoning
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
A Framework for High-Level Feedback to Adaptive, Per-Pixel, Mixture-of-Gaussian Background Models
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A Probabilistic Background Model for Tracking
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Baseline Results for the Challenge Problem of Human ID Using Gait Analysis
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Gait Analysis for Recognition and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Learning Pedestrian Models for Silhouette Refinement
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Segmenting Foreground Objects from a Dynamic Textured Background via a Robust Kalman Filter
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
Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A full-body layered deformable model for automatic model-based gait recognition
EURASIP Journal on Advances in Signal Processing
A model for dynamic object segmentation with kernel density estimation based on gradient features
Image and Vision Computing
A spatially distributed model for foreground segmentation
Image and Vision Computing
Learning static object segmentation from motion segmentation
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Boosting discriminant learners for gait recognition using MPCA features
Journal on Image and Video Processing - Special issue on video-based modeling, analysis, and recognition of human motion
Segmentation of motion objects from surveillance video sequences using partial correlation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Novel micro aerial vehicle video segmentation algorithm
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
International Journal of Computer Vision
Background subtraction with dirichlet processes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
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Many video surveillance and identification applications need to find moving objects in the field of view of a stationary camera. A popular method for obtaining these silhouettes is through the process of background subtraction. We present a novel method for comparing image frames to the model of the stationary background that exploits the spatial and temporal dependencies that objects in motion impose on their images. We achieve this through the development and use of Markov random fields of binary segmentation variates. We show that the MRF approach produces more accurate and visually appealing silhouettes that are less prone to noise and background camouflaging effects than traditional per-pixel based methods. Results include visual examination of silhouettes, comparisons against hand-segmented data, and an analysis of the effects of various silhouette extraction techniques on gait recognition performance.