Strategic Pairwise Selection for Labeling High-Risk Action from Video-Based Data

Accidental risk can occur anywhere in daily life, with typical examples including pedestrian accidents and concerns about child safety on school campuses. In response to these risks, the field of dangerous behavior detection technology has gained considerable attention. Such technology aims to minimize response times and mitigate the occurrence of harm through early detection of potentially dangerous behavior. However, when it comes to generating label data for these models, the diversity of human behavior and the subjective nature of defining dangerous behaviors make the labeling process challenging, often leading to ambiguous situations. To overcome this challenge, we introduce a labeling generation framework based on pair comparison called Strategic Pair Selection (SPS). SPS employs a comparative approach to assist annotators in determining ambiguous cases, thus enhancing the accuracy of the detection of dangerous behavior. Additionally, SPS combines video-based action analysis to learn distinctive features of dangerous behaviors, optimizing the selection of pairs for comparison. The experimental results on real data demonstrate that SPS outperforms other pairwise sampling baseline models, showing its attractive practicability.