SPAMming Labels: Efficient Annotations for the Trackers of Tomorrow
<|reference_start|>SPAMming Labels: Efficient Annotations for the Trackers of Tomorrow: Increasing the annotation efficiency of trajectory annotations from videos has the potential to enable the next generation of data-hungry tracking algorithms to thrive on large-scale datasets. Despite the importance of this task, there are currently very few works exploring how to efficiently label tracking datasets comprehensively. In this work, we introduce SPAM, a video label engine that provides high-quality labels with minimal human intervention. SPAM is built around two key insights: i) most tracking scenarios can be easily resolved. To take advantage of this, we utilize a pre-trained model to generate high-quality pseudo-labels, reserving human involvement for a smaller subset of more difficult instances; ii) handling the spatiotemporal dependencies of track annotations across time can be elegantly and efficiently formulated through graphs. Therefore, we use a unified graph formulation to address the annotation of both detections and identity association for tracks across time. Based on these insights, SPAM produces high-quality annotations with a fraction of ground truth labeling cost. We demonstrate that trackers trained on SPAM labels achieve comparable performance to those trained on human annotations while requiring only $3-20\%$ of the human labeling effort. Hence, SPAM paves the way towards highly efficient labeling of large-scale tracking datasets. We release all models and code.<|reference_end|>
@article{cetintas2024spamming,
    title={SPAMming Labels: Efficient Annotations for the Trackers of Tomorrow},
    author={Orcun Cetintas, Tim Meinhardt, Guillem Bras'o, Laura Leal-Taix'e},
    journal={arXiv preprint arXiv:2404.11426},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11426},
    primaryClass={cs.CV}
}