SearchTrack: Multiple Object Tracking with Object-Customized Search and Motion-Aware Features
This work addresses tracking accuracy for applications like autonomous driving, but it is incremental as it builds on existing methods with specific enhancements.
The paper tackles the association problem in multiple object tracking and segmentation by proposing SearchTrack, which uses object-customized search and motion-aware features, achieving state-of-the-art performance with 71.5 HOTA for cars and 57.6 HOTA for pedestrians on KITTI MOTS and 53.4 HOTA on MOT17.
The paper presents a new method, SearchTrack, for multiple object tracking and segmentation (MOTS). To address the association problem between detected objects, SearchTrack proposes object-customized search and motion-aware features. By maintaining a Kalman filter for each object, we encode the predicted motion into the motion-aware feature, which includes both motion and appearance cues. For each object, a customized fully convolutional search engine is created by SearchTrack by learning a set of weights for dynamic convolutions specific to the object. Experiments demonstrate that our SearchTrack method outperforms competitive methods on both MOTS and MOT tasks, particularly in terms of association accuracy. Our method achieves 71.5 HOTA (car) and 57.6 HOTA (pedestrian) on the KITTI MOTS and 53.4 HOTA on MOT17. In terms of association accuracy, our method achieves state-of-the-art performance among 2D online methods on the KITTI MOTS. Our code is available at https://github.com/qa276390/SearchTrack.