DeepLocalization: Using change point detection for Temporal Action
  Localization
Mohammed Shaiqur Rahman Ibne Farabi Shihab Lynna Chu Anuj Sharma
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.