Detecting Independent Motion: The Statistics of Temporal Continuity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Salient Motion by Accumulating Directionally-Consistent Flow
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
Performance characterisation in computer vision: statistics in testing and design
Imaging and vision systems
Robot Vision
Empirical Evaluation Techniques in Computer Vision
Empirical Evaluation Techniques in Computer Vision
W4S: A real-time system detecting and tracking people in 2 1/2D
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Optimally Rotation-Equivariant Directional Derivative Kernels
CAIP '97 Proceedings of the 7th International Conference on Computer Analysis of Images and Patterns
Finding Periodicity in Space and Time
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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
Scene modeling and change detection in dynamic scenes: A subspace approach
Computer Vision and Image Understanding
A double layer background model to detect unusual events
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
A background subtraction algorithm for detecting and tracking vehicles
Expert Systems with Applications: An International Journal
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Background modeling by subspace learning on spatio-temporal patches
Pattern Recognition Letters
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Video surveillance in outdoor areas is hampered by consistent background motion which defeats systems that use motion to identify intruders. While algorithms exist for masking out regions with motion, a better approach is to develop a statistical model of the typical dynamic video appearance. This allows the detection of potential intruders even in front of trees and grass waving in the wind, waves across a lake, or cars moving past. In this paper we present a general framework for the identification of anomalies in video, and a comparison of statistical models that characterize the local video dynamics at each pixel neighborhood. A real-time implementation of these algorithms runs on an 800MHz laptop, and we present qualitative results in many application domains.