  In recent years, numerous effective multi-object tracking (MOT) methods are
developed because of the wide range of applications. Existing performance
evaluations of MOT methods usually separate the object tracking step from the
object detection step by using the same fixed object detection results for
comparisons. In this work, we perform a comprehensive quantitative study on the
effects of object detection accuracy to the overall MOT performance, using the
new large-scale University at Albany DETection and tRACking (UA-DETRAC)
benchmark dataset. The UA-DETRAC benchmark dataset consists of 100 challenging
video sequences captured from real-world traffic scenes (over 140,000 frames
with rich annotations, including occlusion, weather, vehicle category,
truncation, and vehicle bounding boxes) for object detection, object tracking
and MOT system. We evaluate complete MOT systems constructed from combinations
of state-of-the-art object detection and object tracking methods. Our analysis
shows the complex effects of object detection accuracy on MOT system
performance. Based on these observations, we propose new evaluation tools and
metrics for MOT systems that consider both object detection and object tracking
for comprehensive analysis.
