The KITTI Vision Benchmark Suite

Multi-Object Tracking and Segmentation (MOTS) Evaluation

KITTI MOTS will be part of the RobMOTS Challenge at CVPR 21. Deadline June 11.

The Multi-Object and Segmentation (MOTS) benchmark [2] consists of 21 training sequences and 29 test sequences. It is based on the KITTI Tracking Evaluation 2012 and extends the annotations to the Multi-Object and Segmentation (MOTS) task. To this end, we added dense pixelwise segmentation labels for every object. We evaluate submitted results using the metrics HOTA, CLEAR MOT and MT/PT/ML. We rank methods by HOTA [1]. Our development kit and github evaluation code provides details about the data format as well as utility functions for reading and writing the label files. (adapted for the segmentation case). Evaluation is performed using the code from the TrackEval repository.

We recommend that everyone who uses this benchmark reads this blog post for an overview of the HOTA metrics.

[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.
[2] P. Voigtlaender, M. Krause, A. Os̆ep, J. Luiten, B. Sekar, A. Geiger, B. Leibe: MOTS: Multi-Object Tracking and Segmentation. CVPR 2019.

Note: On 25.02.2020 we have updated the evaluation to use the HOTA metrics as the main evaluation metrics, and to show results as plots to enable better comparison over various aspects of tracking. We have re-calculated the results for all methods. Please download the new evaluation code. Please report these new numbers for all future submissions. The previous leaderboards before the changes will remain live for now, but after some time they will stop being updated. They can be found here.

Please address any questions or feedback about KITTI tracking or KITTI mots evaluation to Jonathon Luiten at luiten@vision.rwth-aachen.de.

Important Policy Update: As more and more non-published work and re-implementations of existing work is submitted to KITTI, we have established a new policy: from now on, only submissions with significant novelty that are leading to a peer-reviewed paper in a conference or journal are allowed. Minor modifications of existing algorithms or student research projects are not allowed. Such work must be evaluated on a split of the training set. To ensure that our policy is adopted, new users must detail their status, describe their work and specify the targeted venue during registration. Furthermore, we will regularly delete all entries that are 6 months old but are still anonymous or do not have a paper associated with them. For conferences, 6 month is enough to determine if a paper has been accepted and to add the bibliography information. For longer review cycles, you need to resubmit your results.

Additional information used by the methods

Stereo: Method uses left and right (stereo) images
Laser Points: Method uses point clouds from Velodyne laser scanner
GPS: Method uses GPS information
Online: Online method (frame-by-frame processing, no latency)
Additional training data: Use of additional data sources for training (see details)