International Journal of Computer Vision
A Two-Step Approach for Detecting Individuals within Dense Crowds
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Robust pedestrian detection and tracking in crowded scenes
Image and Vision Computing
Simultaneous appearance modeling and segmentation for matching people under occlusion
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
3D motion segmentation from straight-line optical flow
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
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
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
Trajectory Analysis and Semantic Region Modeling Using Nonparametric Hierarchical Bayesian Models
International Journal of Computer Vision
Improving detector of Viola and Jones through SVM
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Robust visual tracking for multiple targets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A shape derivative based approach for crowd flow segmentation
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Hierarchical model for joint detection and tracking of multi-target
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Collecting pedestrian trajectories
Neurocomputing
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The main focus of this work is the integration of feature grouping and model based segmentation into one consistent framework. The algorithm is based on partitioning a given set of image features using a likelihood function that is parameterized on the shape and location of potential individuals in the scene. Using a variant of the EM formulation, maximum likelihood estimates of both the model parameters and the grouping are obtained simultaneously. The resulting algorithm performs global optimization and generates accurate results even when decisions can not be made using local context alone. An important feature of the algorithm is that the number of people in the scene is not modeled explicitly. As a result no prior knowledge or assumed distributions are required. The approach is shown to be robust with respect to partial occlusion, shadows, clutter, and can operate over a large range of challenging view angles including those that are parallel to the ground plane. Comparisons with existing crowd segmentation systems are made and the utility of coupling crowd segmentation with a temporal tracking system is demonstrated.