Sign language detection, identifying if someone is signing or not, is
becoming crucially important for its applications in remote conferencing
software and for selecting useful sign data for training sign language
recognition or translation tasks. We argue that the current benchmark data sets
for sign language detection estimate overly positive results that do not
generalize well due to signer overlap between train and test partitions. We
quantify this with a detailed analysis of the effect of signer overlap on
current sign detection benchmark data sets. Comparing accuracy with and without
overlap on the DGS corpus and Signing in the Wild, we observed a relative
decrease in accuracy of 4.17% and 6.27%, respectively. Furthermore, we propose
new data set partitions that are free of overlap and allow for more realistic
performance assessment. We hope this work will contribute to improving the
accuracy and generalization of sign language detection systems.

通过对当前标志检测基准数据集中签约者重叠对准确性的影响进行详细分析，我们提出使用不含重叠的新数据集划分来更真实地评估标志语言检测系统的性能，从而提高其准确性和泛化性能。