  Simple Online and Realtime Tracking (SORT) is a pragmatic approach to
multiple object tracking with a focus on simple, effective algorithms. In this
paper, we integrate appearance information to improve the performance of SORT.
Due to this extension we are able to track objects through longer periods of
occlusions, effectively reducing the number of identity switches. In spirit of
the original framework we place much of the computational complexity into an
offline pre-training stage where we learn a deep association metric on a
large-scale person re-identification dataset. During online application, we
establish measurement-to-track associations using nearest neighbor queries in
visual appearance space. Experimental evaluation shows that our extensions
reduce the number of identity switches by 45%, achieving overall competitive
performance at high frame rates.
