Automatic Learning of an Activity-Based Semantic Scene Model
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
A fast watershed algorithm based on chain code and its application in image segmentation
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Time-focused clustering of trajectories of moving objects
Journal of Intelligent Information Systems
An improved watershed algorithm based on efficient computation of shortest paths
Pattern Recognition
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Determining activity patterns in retail spaces through video analysis
MM '08 Proceedings of the 16th ACM international conference on Multimedia
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Feature extraction based on Laplacian bidirectional maximum margin criterion
Pattern Recognition
Constructing Hierarchical Representations of Indoor Spaces
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Probabilistic Modeling of Scene Dynamics for Applications in Visual Surveillance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Trajectories of Moving Objects in an Uncertain World
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Generalizing edge detection to contour detection for image segmentation
Computer Vision and Image Understanding
Key Point Detection and High Speed Image Registration Using BLoG
IHMSC '10 Proceedings of the 2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics - Volume 02
Learning semantic scene models by trajectory analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Learning semantic scene models from observing activity in visual surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance
IEEE Transactions on Circuits and Systems for Video Technology
Learning motion patterns in unstructured scene based on latent structural information
Journal of Visual Languages and Computing
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Indoor video surveillance is now widely used in government, public, and private facilities. While the capacity to generate such video data is increasing, our ability to derive a coherent scene understanding of the structure of the scene and how it is being utilized, using only motion data, is still lagging behind. This paper proposes a framework for deriving indoor scene structure identifying abnormal motion behavior using only video tracking data, and without requiring a floor plan. The proposed framework, which is data-driven, is based on four sequential processing steps, namely detection of entrance and exit points, the analysis of the connectivity between entrance and exit points, the extraction of mean paths and motion corridors, and the statistical analysis of the length and velocity parameters of motion for the detection of abnormal motion behavior. The paper outlines the proposed framework and demonstrates its implementation using a real-world data set comprising 1138 trajectories.