Heatmap based high speed tiny sport objects tracking based on TrackNet
pip install -r requirements.txt# video file with output
cd Code_Baddy
python predict_video.py --save_weights_path=weights/model_baddy.h5 --input_video_path=path_to_file.mp4 --output_video_path="tracked.mp4" --n_classes=256See the readme file in the LabelingTool directory
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Use the TrackNet_Python.ipynb to create heatmap as Ground Truth, and save heatmap as JPG file. (First Cell)
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Create the training and testing csv files (Second cell) Change the folder path to your custom paths for 1. and 2.
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Copy the training.csv and testing.csv file to Code_Baddy/Code_Custom folder
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After have training images and ground truth, we can start to train the TrackNet model II (Three frame Input)
- Open command line
- Change directory to Code_Baddy/Code_Custom
- Using following command as example, you may need to change the command:
# Train with custom dataset
python train.py --save_weights_path=weights/model.h5 --training_images_name="training.csv" --epochs=500 --n_classes=256 -- input_height=360 --input_width=640 --load_weights=2 --step_per_epochs=200 --batch_size=2* Trained model weight will be save in weights/model.h5
* Detailed explanation
--save_weights_path: Save the weight path
--training_images_name: Training images csv file path, training.csv
--epochs: Epochs be set as 500 in this work
--n_classes: Last layer classes, since the output value of TrackNet is between 0-255, the last layer depth be set as 256
--input_height: Input height be resize as 360 in this work
--input_width: Input width be resize as 640 in this work
--load_weights: If you want to retrain the weights from previous weight, give the number of weight in weights/model. If not, delete it.
--step_per_epochs: Step per Epochs be set as 200 in this work
-batch_size: Batch size be set as 2 in this work
GitHub : TrackNet
Paper : TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications