Many Multi-Object Tracking (MOT) approaches exploit motion information to
associate all the detected objects across frames. However, many methods that
rely on filtering-based algorithms, such as the Kalman Filter, often work well
in linear motion scenarios but struggle to accurately predict the locations of
objects undergoing complex and non-linear movements. To tackle these scenarios,
we propose a motion-based MOT approach with an enhanced temporal motion
predictor, ETTrack. Specifically, the motion predictor integrates a transformer
model and a Temporal Convolutional Network (TCN) to capture short-term and
long-term motion patterns, and it predicts the future motion of individual
objects based on the historical motion information. Additionally, we propose a
novel Momentum Correction Loss function that provides additional information
regarding the motion direction of objects during training. This allows the
motion predictor rapidly adapt to motion variations and more accurately predict
future motion. Our experimental results demonstrate that ETTrack achieves a
competitive performance compared with state-of-the-art trackers on DanceTrack
and SportsMOT, scoring 56.4% and 74.4% in HOTA metrics, respectively.

提出了一种基于运动的多目标跟踪方法，称为 ETTrack，它融合了变换器模型和时间卷积网络，利用历史运动信息预测个体物体的未来运动，并通过新颖的动量修正损失函数提高了预测准确性，实验证明 ETTrack 在 DanceTrack 和 SportsMOT 上取得了与最先进跟踪器竞争性的性能，分别达到 56.4％和 74.4％的 HOTA 指标。