Rapid octree construction from image sequences
CVGIP: Image Understanding
M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
International Journal of Computer Vision
Counting People in Crowds with a Real-Time Network of Simple Image Sensors
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Principal Axis-Based Correspondence between Multiple Cameras for People Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian-Competitive Consistent Labeling for People Surveillance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Multiple Occluding People by Localizing on Multiple Scene Planes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A non-parametric hierarchical model to discover behavior dynamics from tracks
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
A comparative study on multi-person tracking using overlapping cameras
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
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The assignment of multiple person tracks to a set of candidate person locations in overlapping camera views is potentially computationaly intractable, as observables might depend upon visibility order, and thus upon the decision which of the candidate locations represent actual persons and which do not. In this paper, we present an approximate assignment method which consists of two stages. In a hypothesis generation stage, the similarity between track and measurement is based on a subset of observables (appearance, motion) that is independent of the classification of candidate locations. This allows the computation of the K-best assignment in low polynomial time by standard graph matching methods. In a subsequent hypothesis verification stage, the known person positions associated with the K-best solutions are used to define the full set of observables, which are used to compute the maximum likelihood assignment. We demonstrate that our method outperforms the state-of-the-art on a complex outdoor dataset.