MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Behavioral Priors for Detection and Tracking of Pedestrians in Video Sequences
International Journal of Computer Vision
International Journal of Computer Vision
Floor Fields for Tracking in High Density Crowd Scenes
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Feature Correspondence Via Graph Matching: Models and Global Optimization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models
The Journal of Machine Learning Research
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Automatic estimation of movement statistics of people
AMDO'12 Proceedings of the 7th international conference on Articulated Motion and Deformable Objects
GMCP-Tracker: global multi-object tracking using generalized minimum clique graphs
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
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
Destination flow for crowd simulation
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Robust abandoned object detection integrating wide area visual surveillance and social context
Pattern Recognition Letters
Tracking with a mixed continuous-discrete Conditional Random Field
Computer Vision and Image Understanding
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We consider the problem of data association in a multiperson tracking context. In semi-crowded environments, people are still discernible as individually moving entities, that undergo many interactions with other people in their direct surrounding. Finding the correct association is therefore difficult, but higher-order social factors, such as group membership, are expected to ease the problem. However, estimating group membership is a chicken-and-egg problem: knowing pedestrian trajectories, it is rather easy to find out possible groupings in the data, but in crowded scenes, it is often difficult to estimate closely interacting trajectories without further knowledge about groups. To this end, we propose a third-order graphical model that is able to jointly estimate correct trajectories and group memberships over a short time window. A set of experiments on challenging data underline the importance of joint reasoning for data association in crowded scenarios.