Two-stage Transformer: localization of distracting behaviors

Detecting dangerous driving behaviors from cabin video surveillance has become a fundamental pillar for road safety, especially in fleet and inspection center environments. To address this challenge, advanced artificial intelligence architectures have been developed that allow precise localization of distracting actions in video sequences. A paradigmatic example is the two-stage approach based on transformers: a first phase of feature extraction with VideoMAE and a second phase of temporal localization using augmented masked attention (AMA). This design, by integrating modules such as Spatial Pyramid Pooling Fast (SPPF), captures multiple temporal scales, balancing accuracy and computational efficiency. In this context, companies seeking to implement intelligent monitoring solutions can benefit from custom applications that adapt these models to their specific needs, optimizing performance without sacrificing resources. The scalability of these technologies is enhanced through AI for businesses that integrate AI agents capable of processing video streams in real time. Furthermore, the combination of services such as cybersecurity and AWS and Azure cloud services ensures the protection and availability of generated sensitive data. On the other hand, business intelligence services with tools like Power BI allow visualization of obtained behavior metrics, facilitating decision-making. At Q2BSTUDIO, we develop custom software that incorporates these components, from feature extraction to temporal localization, adapting to the requirements of each organization. The key lies in achieving a balance between model capacity (such as ViT-Giant backbones reaching 88% accuracy) and operational efficiency, where lighter alternatives (ViT-base) drastically reduce GFLOPs. Our team implements artificial intelligence solutions that not only identify distracting behaviors but also integrate with process automation platforms. Thus, the localization of distractions while driving ceases to be an isolated technical problem and becomes a strategic component of fleet management, supported by two-stage transformer architectures and a robust technological ecosystem.