  We propose a conceptually simple and thus fast multi-object tracking (MOT)
model that does not require any attached modules, such as the Kalman filter,
Hungarian algorithm, transformer blocks, or graph networks. Conventional MOT
models are built upon the multi-step modules listed above, and thus the
computational cost is high. Our proposed end-to-end MOT model,
\textit{TicrossNet}, is composed of a base detector and a cross-attention
module only. As a result, the overhead of tracking does not increase
significantly even when the number of instances ($N_t$) increases. We show that
TicrossNet runs \textit{in real-time}; specifically, it achieves 32.6 FPS on
MOT17 and 31.0 FPS on MOT20 (Tesla V100), which includes as many as $>$100
instances per frame. We also demonstrate that TicrossNet is robust to $N_t$;
thus, it does not have to change the size of the base detector, depending on
$N_t$, as is often done by other models for real-time processing.
