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.

我们介绍了 DeepLocalization，这是一个创新的框架，专门用于实时监测驾驶行为的定位。通过利用先进的深度学习方法，我们旨在解决分散注意力驾驶这一导致道路事故的严重问题。我们的策略采用了两个方法：利用基于图的变点检测来确定行为的时间点，以及使用视频大型语言模型 (Video-LLM) 来准确分类活动。通过精心的提示工程，我们定制了视频大型语言模型，以处理驾驶活动的微妙之处，确保在稀疏数据情况下的分类效果。我们的框架经过轻量级优化，能够在消费级 GPU 上应用广泛。我们在 SynDD2 数据集上对我们的方法进行了严格测试，这是一个评估分散驾驶行为的复杂基准，结果显示出显着的性能，事件分类准确率为 57.5%，事件检测准确率为 51%。这些结果突显了 DeepLocalization 在准确识别不同驾驶行为及其时间发生的潜力，同时适用于有限的计算资源。