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TrackNet-Badminton-Tracking-tensorflow2

PS: It's not an official implementation ! image

TrackNet

TrackNet is a deep learning network for higi-speed and tiny objects tracking invented by National Chiao-Tung University in Taiwan. It's a FCN model adpotes VGG16 to generate feature map and DeconvNet to decode using pixel-wise classification. TrackNet could take multiple consecutive frames as input, model will learn not only object tracking but also trajectory to enhance its capability of positioning and recognition. TrackNet will generate gaussian heat map centered on ball to indicate position of the ball. Binary cross-entropy is used as loss function to compute difference between heat map of prediction and ground truth.

Modification

1. Combine ResNet and U-Net to form network architecture.

image

Layer Filter size Depth Padding Stride Activation
conv1 3 x 3 64 2 1 BN+Relu
conv2 3 x 3 64 2 1 BN+Relu
resD_1 - 32 - - BN+Relu
resE_1 x 2 - 32 - - BN+Relu
resD_2 - 64 - - BN+Relu
resE_2 x 2 - 64 - - BN+Relu
resD_3 - 128 - - BN+Relu
resE_3 x 3 - 128 - - BN+Relu
resD_4 - 256 - - BN+Relu
resE_4 x 2 - 256 - - BN+Relu
resU_1 + concat - 128+128 - - BN+Relu
resDE_5 x 3 - 128 - - BN+Relu
resU_2 + concat - 64+64 - - BN+Relu
resDE_6 x 2 - 64 - - BN+Relu
resU_3 + concat - 32+32 - - BN+Relu
resDE_7 x 2 - 32 - - BN+Relu
resU_4 - 16 - - BN+Relu
conv3 3 x 3 64 2 1 BN+Relu
conv4 3 x 3 64 2 1 BN+Relu
conv5 3 x 3 256 2 1 BN+Relu+Softmax

Sturcture of res-block-encoder(resE)

Layer Filter size Depth Padding Stride Activation
conv1 1 x 1 n 0 1 BN+Relu
conv2 3 x 3 n 2 1 BN+Relu
conv3 1 x 1 2n 0 1 BN+Relu

Sturcture of res-block-downsamping(resD)

Layer Filter size Depth Padding Stride Activation
conv1 1 x 1 n 0 1 BN+Relu
conv2 3 x 3 n 2 2 BN+Relu
conv3 1 x 1 2n 0 1 BN+Relu

Sturcture of res-block-decoder(resDE)

Layer Filter size Depth Padding Stride Activation
conv1 1 x 1 n 0 1 BN+Relu
conv2 3 x 3 n 2 1 BN+Relu
conv3 1 x 1 n 0 1 BN+Relu

Sturcture of res-block-upsamping(resU)

Layer Filter size Depth Padding Stride Activation
conv1 1 x 1 n 0 1 BN+Relu
convT1 3 x 3 n 0 2 BN+Relu
conv2 1 x 1 n 0 1 BN+Relu

2. Use Conv2Dtranspose instead for upsampling in decoder, matching structure of ResNet in encoder.

3. Use focal loss to help model focusing more on small ground truth.

image

4. Use consecutive 3 frames in gray scale as input image to reduce memory usage and increase training speed.

Parameter of training

Parameter Value
Image size 512 x 288
Heat map ball radius 2.5 pixel
Batch size 2
Learning rate 1.0
Epochs 50
Optimizer Adadelta
Number of training images ~20k

Accuracy, Precision and Recall for test.mp4

TP, FP1, FP2, TN, FN are defined as below:

  • TP: True positive, center distance of ball between prediction and ground truch is smaller than 5 pixel
  • FP1: False positive, center distance of ball between prediction and ground truch is larger than 5 pixel
  • FP2: Fasle positive, if ball is not in ground truth but in prediction.
  • TN: True negative.
  • FN: False positive.
Metric Formula Value
Accuracy (TP+TN)/(TP+TN+FP1+FP2+FN) 0.909
Precision TP/(TP+FP1+FP2) 0.939
Recall TP/(TP+FN) 0.953

Setup

  1. Clone the repository:https://github.com/Chang-Chia-Chi/TrackNet-Badminton-Tracking-tensorflow2.git
  2. Run pip3 install -r requirements.txt to install packages required.
  3. Because the model is created with channel first, it could be trained and tested with GPU only.

Label

image

  1. Run python imgLabel.py to open the program.
  2. Mouse and button events are described below:
Mouse Event Function
Left click Label center of the ball
Middel click Chancel label of the ball
Keyboard Event Function
e exit program
s save csv
n go to next frame
p back to previous frame
f go to first frame
l go to last frame
> fast forward 36 frames
< fast backward 36 frames
  1. If you want to load pre-labeled csv file, change load_csv in imgLabel.py to True.
  2. After label all frames, press s to save file and then press e to leave the program.

Train

folder architecture:

TrackNet-Badminton-Tracking-tensorflow2
|   
|___raw_data    
|       |    
|       |___ <training_videos>, <training_csvs>   
|   
|___train_data(generated from raw_data)    
|       |      
|       |___ <training_images>, <training_heat-maps>    
|   
|_____test    
|       |   
|       |___<test_video>, <test_csv>    
|   
|___weights   
        |   
        |___ <trained_weights>
  1. Put all videos and corresponding csvs in raw_data folder. Make sure name of video and csv pair is identical.
  2. Run python video2img.py to convert videos to trainging images and heat-maps.
  3. Run python train.py --<args> to train the model.

Test

  1. Put video and csv file in test folder and change --video_path and --label_path in parser.py (or indicate when type script).
  2. If you don't want to compute performance of model, set --label_path as empty string "".
  3. Run python predict.py --<args> to test the model.

Reference

  1. https://arxiv.org/abs/1907.03698
    TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications
  2. https://arxiv.org/abs/1708.02002
    Focal Loss for Dense Object Detection
  3. https://nol.cs.nctu.edu.tw:234/open-source/TrackNetv2
    TrackNetV2: Efficient TrackNet (GitLab)