Estimating pedestrian counts in groups
Computer Vision and Image Understanding
A Two-Step Approach for Detecting Individuals within Dense Crowds
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
A People Counting System Based on Face Detection and Tracking in a Video
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Crowd Flow Segmentation Using a Novel Region Growing Scheme
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
An ease-of-use stereo-based particle filter for tracking under occlusion
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
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
Understanding transit scenes: a survey on human behavior-recognition algorithms
IEEE Transactions on Intelligent Transportation Systems
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
A Reliable People Counting System via Multiple Cameras
ACM Transactions on Intelligent Systems and Technology (TIST)
A novel trajectory clustering approach for motion segmentation
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Crowd flow characterization with optimal control theory
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Template matching and monte carlo markova chain for people counting under occlusions
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Visual knowledge transfer among multiple cameras for people counting with occlusion handling
Proceedings of the 20th ACM international conference on Multimedia
Coherent filtering: detecting coherent motions from crowd clutters
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Context and profile based cascade classifier for efficient people detection and safety care system
Multimedia Tools and Applications
Weighted interaction force estimation for abnormality detection in crowd scenes
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Segmentation of Pedestrians with Confidence Level Computation
Journal of Signal Processing Systems
People counting by learning their appearance in a multi-view camera environment
Pattern Recognition Letters
Tracking in dense crowds using prominence and neighborhood motion concurrence
Image and Vision Computing
Counting crowd flow based on feature points
Neurocomputing
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In its full generality, motion analysis of crowded objects necessitates recognition and segmentation of each moving entity. The difficulty of these tasks increases considerably with occlusions and therefore with crowding. When the objects are constrained to be of the same kind, however, partitioning of densely crowded semi-rigid objects can be accomplished by means of clustering tracked feature points. We base our approach on a highly parallelized version of the KLT tracker in order to process the video into a set of feature trajectories. While such a set of trajectories provides a substrate for motion analysis, their unequal lengths and fragmented nature present difficulties for subsequent processing. To address this, we propose a simple means of spatially and temporally conditioning the trajectories. Given this representation, we integrate it with a learned object descriptor to achieve a segmentation of the constituent motions. We present experimental results for the problem of estimating the number of moving objects in a dense crowd as a function of time.