Multiple Object Tracking
In the RAIL-BENCH Multiple Object Tracking challenge, the task is to track all persons across consecutive video frames. All scenes are caputured from a front-facing camera mounted on a train, while it approaches a crowded train station and passes through it.
Dataset
The RAIL-BENCH Tracking dataset consists of 4 video sequences containing between 179 and 249 frames and recorded at 10 fps. In contrast to the other RAIL-BENCH challenges, the entire dataset is treated as the test set. The four sequences are available for download:
Annotation Policy
- Each person is annotated with an axis-aligned bounding box with a unique ID across consecutive frames to capture temporal consistency.
- The occlusion level of each bounding box is indicated with 0 (0-24 %), 1 (25-49 %), 2 (50-74 %), 3 (75-99 %), or 4 (100 %).
- Annotations are terminated for objects that permanently leave the field of view. Objects that reappear — even after being fully occluded for several frames — retain their original ID and are continuously labeled.
- As done for the RAIL-BENCH Object dataset, large crowds of people that cannot be clearly separated into individual instances are annotated with a single bounding box and a special "crowd" flag.
RAIL-BENCH Tracking Challenge
In the RAIL-BENCH Tracking challenge, predictions on the test set are evaluated using the higher order tracking accuracy (HOTA).
How to Participate
- Train your model with any data sources, for instance the RAIL-BENCH Object dataset.
- Download the RAIL-BENCH Tracking dataset and run inference on the test set.
- Soon, we will publish the official CodaBench challenge, where you can submit your predictions and get evaluated on the hidden ground truth annotations.