  Different from existing MOT (Multi-Object Tracking) techniques that usually
aim at improving tracking accuracy and average FPS, real-time systems such as
autonomous vehicles necessitate new requirements of MOT under limited computing
resources: (R1) guarantee of timely execution and (R2) high tracking accuracy.
In this paper, we propose RT-MOT, a novel system design for multiple MOT tasks,
which addresses R1 and R2. Focusing on multiple choices of a workload pair of
detection and association, which are two main components of the
tracking-by-detection approach for MOT, we tailor a measure of object
confidence for RT-MOT and develop how to estimate the measure for the next
frame of each MOT task. By utilizing the estimation, we make it possible to
predict tracking accuracy variation according to different workload pairs to be
applied to the next frame of an MOT task. Next, we develop a novel
confidence-aware real-time scheduling framework, which offers an offline timing
guarantee for a set of MOT tasks based on non-preemptive fixed-priority
scheduling with the smallest workload pair. At run-time, the framework checks
the feasibility of a priority-inversion associated with a larger workload pair,
which does not compromise the timing guarantee of every task, and then chooses
a feasible scenario that yields the largest tracking accuracy improvement based
on the proposed prediction. Our experiment results demonstrate that RT-MOT
significantly improves overall tracking accuracy by up to 1.5x, compared to
existing popular tracking-by-detection approaches, while guaranteeing timely
execution of all MOT tasks.
