Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies

@article{Sadeghian2017TrackingTU,
  title={Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies},
  author={Amir Sadeghian and Alexandre Alahi and Silvio Savarese},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={300-311}
}
The majority of existing solutions to the Multi-Target Tracking (MTT) problem do not combine cues over a long period of time in a coherent fashion. In this paper, we present an online method that encodes long-term temporal dependencies across multiple cues. One key challenge of tracking methods is to accurately track occluded targets or those which share similar appearance properties with surrounding objects. To address this challenge, we present a structure of Recurrent Neural Networks (RNN… CONTINUE READING
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Joint Learning of Convolutional Neural Networks and Temporally Constrained Metrics for Tracklet Association

2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) • 2016

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Learning by Tracking: Siamese CNN for Robust Target Association

2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) • 2016

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