trajnettools 0.3.0

Tools

• summary table and plots: python -m trajnettools.summarize <dataset_files>

• plot sample trajectories: python -m trajnettools.trajectories <dataset_file>

• visualize interactions: python -m trajnettools.visualize_interactions <dataset_file> --interaction_type 'ca'

• obtain distribution of trajectory types: python -m trajnettools.dataset_stats <dataset_file>

APIs

• trajnettools.Reader: class to read the dataset_file

• trajnettools.show: module containing contexts for visualizing rows and paths

• trajnettools.writers: write a trajnet dataset file

• trajnettools.metrics: contains unimodal metrics: average_l2(), final_l2() and collision() and multimodal metrics: topk() and nll() implementations

Dataset

Datasets are split into train, val and test set. Every line is a self contained JSON string (ndJSON).

Scene:

{"scene": {"id": 266, "p": 254, "s": 10238, "e": 10358, "fps": 2.5, "tag": 2}}

Track:

{"track": {"f": 10238, "p": 248, "x": 13.2, "y": 5.85}}

with:

• id: scene id

• p: pedestrian id

• s, e: start and end frame id

• fps: frame rate

• tag: trajectory type

• f: frame id

• x, y: x- and y-coordinate in meters

• pred_number: (optional) prediction number for multiple output predictions

• scene_id: (optional) corresponding scene_id for multiple output predictions

Frame numbers are not recomputed. Rows are resampled to about 2.5 rows per second.

Dev

pylint trajnettools
python -m pytest
# optional: mypy trajnettools --disallow-untyped-defs

Dataset Summaries

biwi_hotel:

crowds_students001:

crowds_students003:

crowds_zara02:

crowds_zara03:

dukemtmc:

syi:

wildtrack:

Interactions

leader_follower:

collision_avoidance:

group:

others: