Recently, one-stage trackers that use a joint model to predict both
detections and appearance embeddings in one forward pass received much
attention and achieved state-of-the-art results on the Multi-Object Tracking
(MOT) benchmarks. However, their success depends on the availability of videos
that are fully annotated with tracking data, which is expensive and hard to
obtain. This can limit the model generalization. In comparison, the two-stage
approach, which performs detection and embedding separately, is slower but
easier to train as their data are easier to annotate. We propose to combine the
best of the two worlds through a data distillation approach. Specifically, we
use a teacher embedder, trained on Re-ID datasets, to generate pseudo
appearance embedding labels for the detection datasets. Then, we use the
augmented dataset to train a detector that is also capable of regressing these
pseudo-embeddings in a fully-convolutional fashion. Our proposed one-stage
solution matches the two-stage counterpart in quality but is 3 times faster.
Even though the teacher embedder has not seen any tracking data during
training, our proposed tracker achieves competitive performance with some
popular trackers (e.g. JDE) trained with fully labeled tracking data.Comment: Workshop on Learning with Limited Labelled Data for Image and Video
Understanding (L3D-IVU), CVPR2022 Worksho