TrackNetV3

We present TrackNetV3, a model composed of two core modules: trajectory prediction and rectification. The trajectory prediction module leverages an estimated background as auxiliary data to locate the shuttlecock in spite of the fluctuating visual interferences. This module also incorporates mixup data augmentation to formulate complex scenarios to strengthen the network’s robustness. Given that a shuttlecock can occasionally be obstructed, we create repair masks by analyzing the predicted trajectory, subsequently rectifying the path via inpainting. [paper]

Performance

• Performance on the test split of Shuttlecock Trajectory Dataset.

Model	Accuracy	Precision	Recall	F1	FPS
YOLOv7	57.82%	78.53%	59.96%	68.00%	34.77
TrackNetV2	94.98%	99.64%	94.56%	97.03%	27.70
TrackNetV3	97.51%	97.79%	99.33%	98.56%	25.11

Installation

• Develop Environment

  Ubuntu 16.04.7 LTS
  Python 3.8.7
  torch 1.10.0
  

• Clone this reposity.

  git clone https://github.com/qaz812345/TrackNetV3.git
  

• Install the requirements.

  pip install -r requirements.txt
  

Inference

• Download the checkpoints
• Unzip the file and place the parameter files to ckpts

  unzip TrackNetV3_ckpts.zip
  

• Predict the label csv from the video

  python predict.py --video_file test.mp4 --tracknet_file ckpts/TrackNet_best.pt --inpaintnet_file ckpts/InpaintNet_best.pt --save_dir prediction
  

• Predict the label csv from the video, and output a video with predicted trajectory

  python predict.py --video_file test.mp4 --tracknet_file ckpts/TrackNet_best.pt --inpaintnet_file ckpts/InpaintNet_best.pt --save_dir prediction --output_video
  

• For large video
  • Enable the --large_video flag to use an IterableDataset instead of the normal Dataset, which prevents memory errors. Note that this will decrease the inference speed.
  • Use --max_sample_num to set the number of samples for background estimation.
  • Use --video_range to specify the start and end seconds of the video for background estimation.

  python predict.py --video_file test.mp4 --tracknet_file ckpts/TrackNet_best.pt --inpaintnet_file ckpts/InpaintNet_best.pt --save_dir prediction --large_video --video_range 324,330
  

Training

1. Prepare Dataset

• Download Shuttlecock Trajectory Dataset
• Set the data root directory to data_dir in dataset.py.
• Data Preprocessing

  python preprocess.py
  

• The frame directories and the val directory will be generated after preprocessing.
• Check the estimated background images in <data_dir>/median
  • If available, the dataset will use the median image of the match; otherwise, it will use the median image of the rally.
  • For example, you can exclude train/match16/median.npz due to camera angle discrepancies; therefore, the dataset will resort to the median image of the rally within match 16.
• The preprocessed dataset will be cached using npy files, so please ensure that you delete these files if you make any modifications to the dataset.
• Dataset File Structure:

  data
    ├─ train
    |   ├── match1/
    |   │   ├── csv/
    |   │   │   ├── 1_01_00_ball.csv
    |   │   │   ├── 1_02_00_ball.csv
    |   │   │   ├── …
    |   │   │   └── *_**_**_ball.csv
    |   │   ├── frame/
    |   │   │   ├── 1_01_00/
    |   │   │   │   ├── 0.png
    |   │   │   │   ├── 1.png
    |   │   │   │   ├── …
    |   │   │   │   └── *.png
    |   │   │   ├── 1_02_00/
    |   │   │   │   ├── 0.png
    |   │   │   │   ├── 1.png
    |   │   │   │   ├── …
    |   │   │   │   └── *.png
    |   │   │   ├── …
    |   │   │   └── *_**_**/
    |   │   │
    |   │   └── video/
    |   │       ├── 1_01_00.mp4
    |   │       ├── 1_02_00.mp4
    |   │       ├── …
    |   │       └── *_**_**.mp4
    |   ├── match2/
    |   │ ⋮
    |   └── match26/
    ├─ val
    |   ├── match1/
    |   ├── match2/
    |   │ ⋮
    |   └── match26/
    └─ test
        ├── match1/
        ├── match2/
        └── match3/

• Attributes in each csv files: Frame, Visibility, X, Y

2. Train Tracking Module

• Train the tracking module from scratch

  python train.py --model_name TrackNet --seq_len 8 --epochs 30 --batch_size 10 --bg_mode concat --alpha 0.5 --save_dir exp --verbose
  

• Resume training (start from the last epoch to the specified epoch)

  python train.py --model_name TrackNet --epochs 30 --save_dir exp --resume_training --verbose
  

3. Generate Predited Trajectories and Inpainting Masks

• Generate predicted trajectories and inpainting masks for training rectification module
  • Noted that the coordinate range corresponds to the input spatial dimensions, not the size of the original image.

  python generate_mask_data.py --tracknet_file ckpts/TrackNet_best.pt --batch_size 16
  

4. Train Rectification Module

• Train the rectification module from scratch.

  python train.py --model_name InpaintNet --seq_len 16 --epoch 300 --batch_size 32 --lr_scheduler StepLR --mask_ratio 0.3 --save_dir exp --verbose
  

• Resume training (start from the last epoch to the specified epoch)

  python train.py --model_name InpaintNet --epochs 30 --save_dir exp --resume_training
  

Evaluation

• Evaluate TrackNetV3 on test set

  python generate_mask_data.py --tracknet_file ckpts/TrackNet_best.pt --split_list test
  python test.py --inpaintnet_file ckpts/InpaintNet_best.pt --save_dir eval
  

• Evaluate the tracking module on test set

  python test.py --tracknet_file ckpts/TrackNet_best.pt --save_dir eval
  

• Generate video with ground truth label and predicted result

  python test.py --tracknet_file ckpts/TrackNet_best.pt --video_file data/test/match1/video/1_05_02.mp4 
  

Error Analysis Interface

• Evaluate TrackNetV3 on test set and save the detail results for error analysis

  python test.py --tracknet_file ckpts/TrackNet_best.pt --inpaintnet_file ckpts/InpaintNet_best.pt --save_dir eval --output_pred
  

• Add json path of evaluation results to the file list in error_analysis.py

  30  # Evaluation result file list
  31  if split == 'train':
  32      eval_file_list = [
  33          {'label': label_name, 'value': json_path},
   ⋮                              ⋮
          ]
      elif split == 'val':
          eval_file_list = [
              {'label': label_name, 'value': json_path},
                                  ⋮
          ]
      elif split == 'test':
          eval_file_list = [
              {'label': label_name, 'value': json_path},
                                  ⋮
          ]
      else:
          raise ValueError(f'Invalid split: {split}')                                  
  

• Run Dash application

  python error_analysis.py --split test --host 127.0.0.1