The dataset comes from the player tracking data provided by STATS SportVU. The data are recorded with a series of cameras that surround the court at 360 degrees and give back a bird-eye view of players’positions. Each play composes of 50 time-steps sampled at 5Hz, where each time-step contains the positions (expressed as (x,y,z) world coordinates) for all the 10 players on the court (5 attackers, 5 defenders) plus the ball.
The dataset can be downloaded here and comes in the
bsk_all_data.zip archive. The zip contains the original dataset as a single numpy array all_data.npy: once
extracted the archive content, put the .npy inside datasets/basket. Then run
cd datasets/scripts
python preprocess_bsk.py --players [ atk | def | all ]
to preprocess the data. This will create the datasets/bakset/<players>/<split> directories with the correct .npy
splits that will be read by the PyTorch custom loader.
Since preprocessing the data when loading the dataset introduces a time overhead, we decided to already save in advance all the needed (preprocessed) information inside the .npy splits, goals included: the custom PyTorch loader will only have to read the arrays and do nothing more. Saving all the information in advance also means that if you wish to change anything from the granularity of the goals grid to the percentages of the splits, you have to preprocess again the dataset and produce the corresponding .npy splits. If this is the case, run again the above script changing the arguments and/or the global variables that you need.
In the given splits, by default, goals are computed fixing a 9x10 grid of 5x5 ft² cells (the half court is assumed 45x50 ft²).
The Stanford Drone Dataset is composed of a series of top-down videos recorded by a hovering drone in 8 different college campus scenes. The datasets collects complex and crowded scenarios with various types of interacting targets: we focus only on pedestrians, still their trajectories are influenced by the moving vehicles and subjects around them. We used the TrajNet benchmark version of the dataset: trajectories are composed of a series of consecutive positions expressed as (x,y) world coordinates and recorded at 2.5FPS.
The dataset can be downloaded here and comes in a
sdd_all_data.zip archive. The zip contains a directory with single .txt files from the single scenes.
Put the all_data directory inside datasets\sdd; then run
cd datasets/scripts
python preprocess_sdd.py
to preprocess the data. This will
- read all the trajectories from the single .txt files
- group them in a single .npy array
- produce the .npy splits from the single array
- save the splits in the
datasets/sdd/sdd_npydirectory
On the contrary of what said for the NBA dataset, the other SDD data preparation phases (like the ground-truth goals extraction) are quite light, therefore are performed inside the PyTorch custom loader when loading the data.
I.e. if you wish to change the granularity of the goals grid or the size of the goals window, you do not need to preprocess again the data but you can directly change the arguments of the corresponding training scripts (for more info on the scripts args, see models/README.md).