# SDTracker: Synthetic Data Based Multi-Object Tracking

Source: [SDTracker: Synthetic Data Based Multi-Object Tracking](<https://www.sota2.com/research/paper/68fa6eea1e209131cea42e09>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:09:40.360Z

Authors: Yingda Guan, Zhengyang Feng, Huiying Chang, Kuo Du, Tingting Li, Min Wang

Published: 2023-03-26T08:21:22.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2303.14653>)

[PDF](<https://arxiv.org/pdf/2303.14653.pdf>)