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
Exploiting the circulant structure of tracking-by-detection with kernels
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Fast and adaptive deep fusion learning for detecting visual objects
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Automatic parameter adaptation for multi-object tracking
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
Online parameter tuning for object tracking algorithms
Image and Vision Computing
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Multiple object tracking has been formulated recently as a global optimization problem, and solved efficiently with optimal methods such as the Hungarian Algorithm. A severe limitation is the inability to model multiple objects that are merged into a single measurement, and track them as a group, while retaining optimality. This work presents a new graph structure that encodes these multiple-match events as standard one-to-one matches, allowing computation of the solution in polynomial time. Since identities are lost when objects merge, an efficient method to identify groups is also presented, as a flow circulation problem. The problem of tracking individual objects across groups is then posed as a standard optimal assignment. Experiments show increased performance on the PETS 2006 and 2009 datasets compared to state-of-the-art algorithms.