SPAMming Labels: Efficient Annotations for the Trackers of Tomorrow

arXiv:2404.11426v1 Announce Type: new
Abstract: 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 tracking data engine that provides high-quality labels with minimal human intervention. SPAM is built around two key insights: i) most tracking scenarios …

abstract algorithms annotation annotations arxiv cs.cv data datasets efficiency importance labels next scale spamming tomorrow tracking trajectory type videos