Spatially supervised recurrent convolutional neural networks for visual object tracking

@article{Ning2017SpatiallySR,
  title={Spatially supervised recurrent convolutional neural networks for visual object tracking},
  author={G. Ning and Zhi Zhang and Chen Huang and Xiaobo Ren and H. Wang and Canhui Cai and Z. He},
  journal={2017 IEEE International Symposium on Circuits and Systems (ISCAS)},
  year={2017},
  pages={1-4}
}
  • G. Ning, Zhi Zhang, +4 authors Z. He
  • Published 2017
  • Computer Science
  • 2017 IEEE International Symposium on Circuits and Systems (ISCAS)
  • In this paper, we develop a new approach of spatially supervised recurrent convolutional neural networks for visual object tracking. Our recurrent convolutional network exploits the history of locations as well as the distinctive visual features learned by the deep neural networks. Inspired by recent bounding box regression methods for object detection, we study the regression capability of Long Short-Term Memory (LSTM) in the temporal domain, and propose to concatenate high-level visual… CONTINUE READING
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