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Waymo Open Dataset
Challenge

2D Tracking

Overview

Given a temporal sequence (prior frames) of camera images, produce a set of 2D boxes and the correspondences between boxes across frames. You may use the baseline object detections linked below, if you wish.

Leaderboard

Submit

Instructions

To submit your entry to the leaderboard, upload your file in the format specified in the Submission protos. This challenge does not have any awards. You can only submit against the Test Set 3 times every 30 days. (Submissions that error out do not count against this total.)

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Metrics

Leaderboard ranking for this challenge is by MOTA/L2 for Object Type "ALL_NS" (all objects except signs: Vehicle, Cyclist, and Pedestrian) with cameras only and any number of prior frames (no future frames).

Primary metric

Multiple Object Tracking Accuracy (MOTA): Accounts for all object configuration errors made by the tracker, false positives, misses, mismatches, over all frames. See this paper for details.

Secondary metric

Multiple Object Tracking Precision (MOTP): The total error in estimated position for matched object-hypothesis pairs over all frames, averaged by the total number of matches made. It shows the ability of the tracker to estimate precise object positions, independent of its skill at recognizing object configurations, keeping consistent trajectories. See this paper for details.False Positive ratio: The number of false positives (all tracker hypotheses for which no real object exists) divided by the total number of objects. See this paper for details.Miss ratio: The number of misses (all objects for which no hypothesis was output) divided by the total number of objects. See this paper for details.Mismatch ratio: The number of mismatches (all occurences where the tracking hypothesis for an object changed compared to previous frames) divided by the total number of objects. See this paper for details.

Sensor Names

C: All cameras
I: Invalid

Label Difficulty Breakdown

Each ground truth label is categorized into different difficulty levels (two levels for now):

  • LEVEL_1, if not marked as LEVEL_2 in the released data.

  • LEVEL_2, if marked as LEVEL_2 in the released data. When evaluating, LEVEL_2 metrics are computed by considering both LEVEL_1 and LEVEL_2 ground truth.

Metric Breakdown

The following metric breakdowns are supported:

  • OBJECT_TYPE: Breakdown by object type ("ALL_NS" refers to all objects except signs: Vehicle, Cyclist, and Pedestrian)

  • RANGE: Breakdown by the distance between object center and vehicle frame origin. [0, 35m), [35m, 50m), [50m, +inf)

Baseline Object Detections

If you want to focus only on the tracking part of this challenge, we are providing baseline object detections that you are free to use. These were generated from Faster R-CNN and the format is the same as the Submission format.