DriveTrack: A Benchmark for Long-Range Point Tracking in Real-World
  Videos
Arjun Balasingam Joseph Chandler Chenning Li Zhoutong Zhang Hari Balakrishnan
This paper presents DriveTrack, a new benchmark and data generation framework
for long-range keypoint tracking in real-world videos. DriveTrack is motivated
by the observation that the accuracy of state-of-the-art trackers depends
strongly on visual attributes around the selected keypoints, such as texture
and lighting. The problem is that these artifacts are especially pronounced in
real-world videos, but these trackers are unable to train on such scenes due to
a dearth of annotations. DriveTrack bridges this gap by building a framework to
automatically annotate point tracks on autonomous driving datasets. We release
a dataset consisting of 1 billion point tracks across 24 hours of video, which
is seven orders of magnitude greater than prior real-world benchmarks and on
par with the scale of synthetic benchmarks. DriveTrack unlocks new use cases
for point tracking in real-world videos. First, we show that fine-tuning
keypoint trackers on DriveTrack improves accuracy on real-world scenes by up to
7%. Second, we analyze the sensitivity of trackers to visual artifacts in real
scenes and motivate the idea of running assistive keypoint selectors alongside
trackers.