ETTrack: Enhanced Temporal Motion Predictor for Multi-Object Tracking
This addresses tracking challenges in dynamic scenes like sports and dance, but is incremental as it builds on existing motion-based methods.
The paper tackled the problem of inaccurate motion prediction in multi-object tracking for non-linear movements by proposing ETTrack, which integrates a transformer and TCN with a novel loss function, achieving 56.4% HOTA on DanceTrack and 74.4% on SportsMOT.
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.