Soccer Ball Detection using Deep CNN

Official implementation of the paper "Utilizing Temporal Information in Deep Convolutional Network for Efficient Soccer Ball Detection and Tracking" (link)

Usage

prepare datasets

python prepare_dataset.py

to reproduce best numbers

python test.py --reproduce=best

to reproduce all numbers

python test.py --reproduce=all

to evaluate on new data only to get output detection of the swetynet

python test.py --dataset=new_sweaty --data_root=/root_folder_of_dataset/

to evaluate on new sequence of data

python test.py --dataset=new_seq --data_root=/root_folder_of_dataset/

structure for the new data should be like in testDataset where each line of the ball.txt is relative path to the image, y center position, x center position, y resolution of image, x resolutionn of image

the result output of the network you can find in the folder 'seq_output'. The target heatmaps on the visualization consist of only zeros due to the implementation of the dataset. Make sure that the number of images in the new dataset is more than 20 if you use --dataset=new_seq.

Implementation Details

py_models/joined_model.py

py_models/lstm.py

py_models/tcn_ed.py

py_train/evaluator.py

py_dataset/seq_dataset.py

arguments.py

Reference

@inproceedings{Kukleva,
  title={Utilizing Temporal Information in Deep Convolutional Network for Efficient Soccer Ball Detection and Tracking },
  author={Kukleva, Anna and Asif Khan, Mohammad and Farazi, Hafez and Behnke, Sven},
  booktitle={Accepted for 23th RoboCup International Symposium, Sydney, Australia, to appear July 2019. },
  year={2019}
}