This code implements a tracker with binary RBM observation model combined with an region selection model based on reinforcement learning algorithms. Here, we provide the code to do the experiments for the single target synthetic experiment reported in our paper.
Learning attentional policies for object tracking and recognition in video with deep networks, L. Bazzani, N. de Freitas, H. Larochelle, V. Murino, and J-A Ting, In International Conference on Machine Learning (ICML), 2011.
##INSTRUCTIONS In order to run the experiments using this package, you should follow these steps:
- Train the binary RBM on soft data with
binRBM_GAZE_train_allMNIST.m
- Train the multiclass logistic classifier
logreg_GAZE_binRBM_MNIST_all.m
- Build your own video using
build_synth_dataset.m
or you can use the provided videos at https://www.lorisbazzani.info/datasets/ICML11_synth_dataset.zip - Run the tracking script
main_synth_GAZE_RBMtracking.m
- Watch the final results
display_final_res.m
at the end of the tracking script - Compare the accuracy of tracking between the three policies in several experiments
error_estimation.m
##3RD-PART LIBRARIES We provide the binary Restricted Bolzmann Machine library written by Kevin Swersky.
##REFERENCE AND ACKNOWLEDGEMENTS
@inproceedings{Bazzani:2011ICML,
author = {Bazzani, Loris and de Freitas, Nando and Larochelle, Hugo, and Murino, Vittorio and Ting, Jo-Anne},
title = {Learning attentional policies for tracking and recognition in video with deep networks},
booktitle = {International Conference on Machine Learning},
year = {2011},
address = {Seattle, WA, United States},
}
The code has been written during the period I spent at UBC, British Columbia, Vancouver, under the supervision of Prof. Nando de Freitas. We thank Kevin Swersky for providing the code for binary RBMs' training and testing.