KMC_cvprw_2017

This is the code for the paper "Kernalised Multi-resolution Convnet for Visual Tracking"

Purpose

Code for monocular, generic object tracking.

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Gist: Kernalised Correlation Filter -> Convnet Prediction

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by Di WU: stevenwudi@gmail.com, 2017/05/01

Citation

If you use this toolbox as part of a research project, please cite the corresponding paper

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@inproceedings{wu2017cvprw,
  title={Kernalised Multi-resolution Convnet for Visual Tracking},
  author={Wu, Di and Wenbin, Zou and Xia, Li and Yong, Zhao},
  booktitle={Proc. Conference on Computer Vision and Pattern Recognition (CVPR) Workshop},
  year={2017}
}

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Dependency: Keras

Some dependent libraries requirements: Keras: for deep learning libarary: https://github.com/fchollet/keras Backend: tensorflow

Test

To reproduce the experimental result for test submission, you need to download the trained model from: https://drive.google.com/open?id=0BzicoAl6Jud9WTNDS0RFUEpkQ1E

there is a Python file:

....py

Train

To train the network, you first need to extract the CNN from the OTB2015:

1)step_1_OTB_100_collect_CNN.py

Voila, here you go.

Dataset

According to some reader recommendation, I supplement the links of the datasets used in the paper as follows:

1. OTB-2015 Dataset --> http://cvlab.hanyang.ac.kr/tracker_benchmark

2. UAV123Dataset --> https://ivul.kaust.edu.sa/Pages/pub-benchmark-simulator-uav.aspx

Contact

If you read the code and find it really hard to understand, please send feedback to: stevenwudi@gmail.com Thank you!