International Journal of Computer Vision
Machine Vision and Applications
Learning to Look at Humans -- What Are the Parts of a Moving Body?
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
A Two-Step Approach for Detecting Individuals within Dense Crowds
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera
International Journal of Computer Vision
A local-motion-based probabilistic model for visual tracking
Pattern Recognition
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Tracking in a Dense Crowd Using Multiple Cameras
International Journal of Computer Vision
Compositional object recognition, segmentation, and tracking in video
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Feature clustering for vehicle detection and tracking in road traffic surveillance
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Human detection in a challenging situation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Crowd counting and segmentation in visual surveillance
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Object segmentation by long term analysis of point trajectories
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
A streakline representation of flow in crowded scenes
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
A method for counting moving people in video surveillance videos
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
Learning scene entries and exits using coherent motion regions
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Multiple-object tracking in cluttered and crowded public spaces
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Trajectory Analysis and Semantic Region Modeling Using Nonparametric Hierarchical Bayesian Models
International Journal of Computer Vision
Visual crowd surveillance through a hydrodynamics lens
Communications of the ACM
Loveparade 2010: Automatic video analysis of a crowd disaster
Computer Vision and Image Understanding
Crowd flow characterization with optimal control theory
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
II-LK – a real-time implementation for sparse optical flow
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking
Automatica (Journal of IFAC)
Visual knowledge transfer among multiple cameras for people counting with occlusion handling
Proceedings of the 20th ACM international conference on Multimedia
Collecting pedestrian trajectories
Neurocomputing
Coherent filtering: detecting coherent motions from crowd clutters
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Exploiting pedestrian interaction via global optimization and social behaviors
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
SuperFloxels: a mid-level representation for video sequences
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Context and profile based cascade classifier for efficient people detection and safety care system
Multimedia Tools and Applications
Video segmentation with superpixels
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Weighted interaction force estimation for abnormality detection in crowd scenes
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Detecting bipedal motion from correlated probabilistic trajectories
Pattern Recognition Letters
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While crowds of various subjects may offer applicationspecific cues to detect individuals, we demonstrate that for the general case, motion itself contains more information than previously exploited. This paper describes an unsupervised data driven Bayesian clustering algorithm which has detection of individual entities as its primary goal. We track simple image features and probabilistically group them into clusters representing independently moving entities. The numbers of clusters and the grouping of constituent features are determined without supervised learning or any subject-specific model. The new approach is instead, that space-time proximity and trajectory coherence through image space are used as the only probabilistic criteria for clustering. An important contribution of this work is how these criteria are used to perform a one-shot data association without iterating through combinatorial hypotheses of cluster assignments. Our proposed general detection algorithm can be augmented with subject-specific filtering, but is shown to already be effective at detecting individual entities in crowds of people, insects, and animals. This paper and the associated video examine the implementation and experiments of our motion clustering framework.