Corpus ID: 6322767

Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project

@article{Zhang2017MultiTargetMT,
  title={Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project},
  author={Zhimeng Zhang and Jianan Wu and Xuan Zhang and Chi Zhang},
  journal={ArXiv},
  year={2017},
  volume={abs/1712.09531}
}
Although many methods perform well in single camera tracking, multi-camera tracking remains a challenging problem with less attention. DukeMTMC is a large-scale, well-annotated multi-camera tracking benchmark which makes great progress in this field. This report is dedicated to briefly introduce our method on DukeMTMC and show that simple hierarchical clustering with well-trained person re-identification features can get good results on this dataset. 
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References

SHOWING 1-5 OF 5 REFERENCES
Multi-camera Multi-Object Tracking
TLDR
This work model's the tracking problem as a global graph, and adopts Generalized Maximum Multi Clique optimization problem as the core algorithm to take both across frame and across camera data correlation into account all together. Expand
Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking
TLDR
The current trends and weaknesses of multiple people tracking methods are shown, and pointers of what researchers should be focusing on to push the field forward are provided. Expand
Fast R-CNN
  • Ross B. Girshick
  • Computer Science
  • 2015 IEEE International Conference on Computer Vision (ICCV)
  • 2015
This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deepExpand
Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features
TLDR
It is shown how both an object class specific representation and a discriminative recognition model can be learned using the AdaBoost algorithm, which allows many different kinds of simple features to be combined into a single similarity function. Expand
The Hungarian Method for the Assignment Problem
  • H. Kuhn
  • Computer Science
  • 50 Years of Integer Programming
  • 2010
This paper has always been one of my favorite “children,” combining as it does elements of the duality of linear programming and combinatorial tools from graph theory. It may be of some interest toExpand