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This paper presents DriveTrack, a new benchmark and data generation frameworkfor long-range keypoint tracking in real-world videos. DriveTrack is motivatedby the observation that the accuracy of state-of-the-art trackers dependsstrongly on visual attributes around the selected keypoints, such as textureand lighting. The problem is that these artifacts are especially pronounced inreal-world videos, but these trackers are unable to train on such scenes due toa dearth of annotations. DriveTrack bridges this gap by building a framework toautomatically annotate point tracks on autonomous driving datasets. We releasea dataset consisting of 1 billion point tracks across 24 hours of video, whichis seven orders of magnitude greater than prior real-world benchmarks and onpar with the scale of synthetic benchmarks. DriveTrack unlocks new use casesfor point tracking in real-world videos. First, we show that fine-tuningkeypoint trackers on DriveTrack improves accuracy on real-world scenes by up to7%. Second, we analyze the sensitivity of trackers to visual artifacts in realscenes and motivate the idea of running assistive keypoint selectors alongsidetrackers.

DriveTrack: A Benchmark for Long-Range Point Tracking in Real-World Videos