  End-to-end production of object tracklets from high resolution video in
real-time and with high accuracy remains a challenging problem due to the cost
of object detection on each frame. In this work we present Localization-based
Tracking (LBT), an extension to any tracker that follows the tracking by
detection or joint detection and tracking paradigms. Localization-based
Tracking focuses only on regions likely to contain objects to boost detection
speed and avoid matching errors. We evaluate LBT as an extension to two example
trackers (KIOU and SORT) on the UA-DETRAC and MOT20 datasets. LBT-extended
trackers outperform all other reported algorithms in terms of PR-MOTA, PR-MOTP,
and mostly tracked objects on the UA-DETRAC benchmark, establishing a new
state-of-the art. relative to tracking by detection with KIOU, LBT-extended
KIOU achieves a 25% higher frame-rate and is 1.1% more accurate in terms of
PR-MOTA on the UA-DETRAC dataset. LBT-extended SORT achieves a 62% speedup and
a 3.2% increase in PR-MOTA on the UA-DETRAC dataset. On MOT20, LBT-extended
KIOU has a 50% higher frame-rate than tracking by detection and is 0.4% more
accurate in terms of MOTA. As of submission time, our LBT-extended KIOU tracker
places 10th overall on the MOT20 benchmark.
