  Physical processes, camera movement, and unpredictable environmental
conditions like the presence of dust can induce noise and artifacts in video
feeds. We observe that popular unsupervised MOT methods are dependent on
noise-free inputs. We show that the addition of a small amount of artificial
random noise causes a sharp degradation in model performance on benchmark
metrics. We resolve this problem by introducing a robust unsupervised
multi-object tracking (MOT) model: AttU-Net. The proposed single-head attention
model helps limit the negative impact of noise by learning visual
representations at different segment scales. AttU-Net shows better unsupervised
MOT tracking performance over variational inference-based state-of-the-art
baselines. We evaluate our method in the MNIST-MOT and the Atari game video
benchmark. We also provide two extended video datasets: ``Kuzushiji-MNIST MOT''
which consists of moving Japanese characters and ``Fashion-MNIST MOT'' to
validate the effectiveness of the MOT models.
