  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.
