PERCOMW '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications Workshops
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Euclidean path modeling for video surveillance
Image and Vision Computing
Trajectory Modeling Using Mixtures of Vector Fields
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Towards Generic Detection of Unusual Events in Video Surveillance
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Hierarchical Matching of 3D Pedestrian Trajectories for Surveillance Applications
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Learning People Trajectories Using Semi-directional Statistics
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
International Journal of Robotics Research
Finding long and similar parts of trajectories
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Incremental learning of statistical motion patterns with growing hidden Markov models
IEEE Transactions on Intelligent Transportation Systems
A dynamic hierarchical clustering method for trajectory-based unusual video event detection
IEEE Transactions on Image Processing
Syntactic matching of trajectories for ambient intelligence applications
IEEE Transactions on Multimedia
Near-optimal mosaic selection for rotating and zooming video cameras
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Trajectory analysis in natural images using mixtures of vector fields
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Content-based retrieval of functional objects in video using scene context
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
A data-driven approach for event prediction
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Functional scene element recognition for video scene analysis
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Abnormality detection using low-level co-occurring events
Pattern Recognition Letters
Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance
Image and Vision Computing
A video surveillance method based on information granularity
EE'07 Proceedings of the 2nd IASME/WSEAS international conference on Energy and environment
Detecting anomalies in people's trajectories using spectral graph analysis
Computer Vision and Image Understanding
Adaptive human motion analysis and prediction
Pattern Recognition
Trajectory analysis using switched motion fields: a parametric approach
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Detecting customers' buying events on a real-life database
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Trajectory Analysis and Semantic Region Modeling Using Nonparametric Hierarchical Bayesian Models
International Journal of Computer Vision
Semantic classification of human behaviors in video surveillance systems
WSEAS Transactions on Computers
Toward visually inferring the underlying causal mechanism in a traffic-light-controlled crossroads
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
NNCluster: an efficient clustering algorithm for road network trajectories
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
An entropy approach for abnormal activities detection in video streams
Pattern Recognition
Crowd behavior surveillance using bhattacharyya distance metric
CompIMAGE'10 Proceedings of the Second international conference on Computational Modeling of Objects Represented in Images
Intelligent multi-camera video surveillance: A review
Pattern Recognition Letters
Learning common behaviors from large sets of unlabeled temporal series
Image and Vision Computing
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
On the use of a minimal path approach for target trajectory analysis
Pattern Recognition
Pattern Recognition Letters
Learning motion patterns in unstructured scene based on latent structural information
Journal of Visual Languages and Computing
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This paper proposes a novel method for detecting nonconforming trajectories of objects as they pass through a scene. Existing methods mostly use spatial features to solve this problem. Using only spatial information is not adequate; we need to take into consideration velocity and curvature information of a trajectory along with the spatial information for an elegant solution. Our method has the ability to distinguish between objects traversing spatially dissimilar paths, or objects traversing spatially proximal paths but having different spatio-temporal characteristics. The method consists of a path building training phase and a testing phase. During the training phase, we use graph-cuts for clustering the trajectories, where the Hausdorff distance metric is used to calculate the edge weights. Each cluster represents a path. An envelope boundary and an average trajectory are computed for each path. During the testing phase we use three features for trajectory matching in a hierarchical fashion. The first feature measures the spatial similarity while the second feature compares the velocity characteristics of trajectories. Finally, the curvature features capture discontinuities in velocity, acceleration, and position of the trajectory. We use real-world pedestrian sequences to demonstrate the practicality of our method.