  In this study, we introduce DeepLocalization, an innovative framework devised
for the real-time localization of actions tailored explicitly for monitoring
driver behavior. Utilizing the power of advanced deep learning methodologies,
our objective is to tackle the critical issue of distracted driving-a
significant factor contributing to road accidents. Our strategy employs a dual
approach: leveraging Graph-Based Change-Point Detection for pinpointing actions
in time alongside a Video Large Language Model (Video-LLM) for precisely
categorizing activities. Through careful prompt engineering, we customize the
Video-LLM to adeptly handle driving activities' nuances, ensuring its
classification efficacy even with sparse data. Engineered to be lightweight,
our framework is optimized for consumer-grade GPUs, making it vastly applicable
in practical scenarios. We subjected our method to rigorous testing on the
SynDD2 dataset, a complex benchmark for distracted driving behaviors, where it
demonstrated commendable performance-achieving 57.5% accuracy in event
classification and 51% in event detection. These outcomes underscore the
substantial promise of DeepLocalization in accurately identifying diverse
driver behaviors and their temporal occurrences, all within the bounds of
limited computational resources.
