Online learned discriminative part-based appearance models for multi-human tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
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
To track or to detect? an ensemble framework for optimal selection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Multi-person tracking-by-detection based on calibrated multi-camera systems
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Online multi-target tracking by large margin structured learning
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Spatio-Temporal clustering model for multi-object tracking through occlusions
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Multiple human tracking system for unpredictable trajectories
Machine Vision and Applications
Multi-Target Tracking by Online Learning a CRF Model of Appearance and Motion Patterns
International Journal of Computer Vision
Online parameter tuning for object tracking algorithms
Image and Vision Computing
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We propose to formulate multi-target tracking as minimization of a continuous energy function. Other than a number of recent approaches we focus on designing an energy function that represents the problem as faithfully as possible, rather than one that is amenable to elegant optimization. We then go on to construct a suitable optimization scheme to find strong local minima of the proposed energy. The scheme extends the conjugate gradient method with periodic trans-dimensional jumps. These moves allow the search to escape weak minima and explore a much larger portion of the variable-dimensional search space, while still always reducing the energy. To demonstrate the validity of this approach we present an extensive quantitative evaluation both on synthetic data and on six different real video sequences. In both cases we achieve a significant performance improvement over an extended Kalman filter baseline as well as an ILP-based state-of-the-art tracker.