SDTracker: Synthetic Data Based Multi-Object Tracking
This addresses multi-object tracking for computer vision applications, but is incremental as it builds on existing synthetic data and pseudo-labeling techniques.
The paper tackles multi-object tracking in real-world scenes by using synthetic data with style randomization and pseudo-labeling on unlabeled data, achieving 61.4 HOTA on MOT17.
We present SDTracker, a method that harnesses the potential of synthetic data for multi-object tracking of real-world scenes in a domain generalization and semi-supervised fashion. First, we use the ImageNet dataset as an auxiliary to randomize the style of synthetic data. With out-of-domain data, we further enforce pyramid consistency loss across different "stylized" images from the same sample to learn domain invariant features. Second, we adopt the pseudo-labeling method to effectively utilize the unlabeled MOT17 training data. To obtain high-quality pseudo-labels, we apply proximal policy optimization (PPO2) algorithm to search confidence thresholds for each sequence. When using the unlabeled MOT17 training set, combined with the pure-motion tracking strategy upgraded via developed post-processing, we finally reach 61.4 HOTA.