  Multi-Object Tracking (MOT) is one of the most fundamental computer vision
tasks that contributes to various video analysis applications. Despite the
recent promising progress, current MOT research is still limited to a fixed
sampling frame rate of the input stream. In fact, we empirically found that the
accuracy of all recent state-of-the-art trackers drops dramatically when the
input frame rate changes. For a more intelligent tracking solution, we shift
the attention of our research work to the problem of Frame Rate Agnostic MOT
(FraMOT), which takes frame rate insensitivity into consideration. In this
paper, we propose a Frame Rate Agnostic MOT framework with a Periodic training
Scheme (FAPS) to tackle the FraMOT problem for the first time. Specifically, we
propose a Frame Rate Agnostic Association Module (FAAM) that infers and encodes
the frame rate information to aid identity matching across multi-frame-rate
inputs, improving the capability of the learned model in handling complex
motion-appearance relations in FraMOT. Moreover, the association gap between
training and inference is enlarged in FraMOT because those post-processing
steps not included in training make a larger difference in lower frame rate
scenarios. To address it, we propose Periodic Training Scheme (PTS) to reflect
all post-processing steps in training via tracking pattern matching and fusion.
Along with the proposed approaches, we make the first attempt to establish an
evaluation method for this new task of FraMOT in two different modes, i.e.,
known frame rate and unknown frame rate, aiming to handle a more complex
situation. The quantitative experiments on the challenging MOT17/20 dataset
(FraMOT version) have clearly demonstrated that the proposed approaches can
handle different frame rates better and thus improve the robustness against
complicated scenarios.
