A Noniterative Greedy Algorithm for Multiframe Point Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving data association by joint modeling of pedestrian trajectories and groupings
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Multiple Object Tracking Using K-Shortest Paths Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Who are you with and where are you going?
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Multiobject tracking as maximum weight independent set
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Multi-target tracking by continuous energy minimization
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Stable multi-target tracking in real-time surveillance video
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Part-based multiple-person tracking with partial occlusion handling
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Globally optimal solution to multi-object tracking with merged measurements
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Exploiting the circulant structure of tracking-by-detection with kernels
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Multi-Target Tracking by Online Learning a CRF Model of Appearance and Motion Patterns
International Journal of Computer Vision
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Data association is an essential component of any human tracking system. The majority of current methods, such as bipartite matching, incorporate a limited-temporal-locality of the sequence into the data association problem, which makes them inherently prone to IDswitches and difficulties caused by long-term occlusion, cluttered background, and crowded scenes.We propose an approach to data association which incorporates both motion and appearance in a global manner. Unlike limited-temporal-locality methods which incorporate a few frames into the data association problem, we incorporate the whole temporal span and solve the data association problem for one object at a time, while implicitly incorporating the rest of the objects. In order to achieve this, we utilize Generalized Minimum Clique Graphs to solve the optimization problem of our data association method. Our proposed method yields a better formulated approach to data association which is supported by our superior results. Experiments show the proposed method makes significant improvements in tracking in the diverse sequences of Town Center [1], TUD-crossing [2], TUD-Stadtmitte [2], PETS2009 [3], and a new sequence called Parking Lot compared to the state of the art methods.