Convolutional restricted Boltzmann machines learning for robust visual tracking

@article{Lei2014ConvolutionalRB,
  title={Convolutional restricted Boltzmann machines learning for robust visual tracking},
  author={Jun Lei and Guohui Li and Dan Tu and Qiang Guo},
  journal={Neural Computing and Applications},
  year={2014},
  volume={25},
  pages={1383-1391}
}
It is a critical step to choose visual features in object tracking. Most existing tracking approaches adopt handcrafted features, which greatly depend on people’s prior knowledge and easily become invalid in other conditions where the scene structures are different. On the contrary, we learn informative and discriminative features from image data of tracking scenes itself. Local receptive filters and weight sharing make the convolutional restricted Boltzmann machines (CRBM) suit for natural… CONTINUE READING