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README.md

TNT

Environment setting is follows.
Operating system: windows, need to change (from scipy.io import loadmat in TNT/train_cnn_trajectory_2d.py) to (h5py) in Linux.
Python: 3.5.4
tensorflow: 1.4.0
cuda: 8.0
cudnn: 5.1.10
opencv: 3.2.0
Other packages: numpy, pickle, sklearn, scipy, matplotlib, PIL.

2D Tracking Training

  1. Prepare the Data.
    Ground truth tracking file: follow the format of MOT (https://motchallenge.net/).
    The frame index and object index are from 1 (not 0) for both tracking ground truth and video frames.
  2. Convert MOT fromat to UA-Detrac format.
    TNT/General/MOT_to_UA_Detrac.m
  3. Crop the ground truth detection into individual bounding box images.
    TNT/General/crop_UA_Detrac.m
  4. Create validation pairs for FaceNet.
    TNT/General/create_pair.m
  5. Train the triplet appearance model based on FaceNet using the cropped data.
    See https://github.com/davidsandberg/facenet.
    All the useful scource code are in TNT/src/.
  6. Train 2D tracking.
    Set directory paths in TNT/train_cnn_trajectory_2d.py before the definition of all the functions.
    Change the sample probability (sample_prob) according to your data density. The number of element in sample_prob is the number of your input Mat files.
    Set the learning rate (lr) to 1e-3 at the beginning. Every 2000 steps, decrease lr by 10 times until it reaches 1e-5.
    The output model will be stored in save_dir.
  7. Run python TNT/train_cnn_trajectory_2d.py.

2D Tracking Testing

  1. Prepare the detection data.
    follow the format of MOT (https://motchallenge.net/).
    The frame index and object index are from 1 (not 0) for both tracking ground truth and video frames.
  2. Set your data and model paths correctly on the top of TNT/tracklet_utils_3c.py.
  3. Set the file_len to be the string length of your input frame name before the extension.
  4. Adjust the tracking parameters in track_struct['track_params'] of TNT/tracklet_utils_3c.py in the function TC_tracker().
  5. Run python TNT/TC_tracker.py.

Citation

Use this bibtex to cite this repository:

@inproceedings{wang2019exploit,
  title={Exploit the connectivity: Multi-object tracking with trackletnet},
  author={Wang, Gaoang and Wang, Yizhou and Zhang, Haotian and Gu, Renshu and Hwang, Jenq-Neng},
  booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
  pages={482--490},
  year={2019},
  organization={ACM}
}
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