Abstract
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.
Abstract (translated)
在这项研究中，我们引入了DeepLocalization，一种专为实时定位针对监控驾驶员行为的动作的创新框架。利用先进深度学习方法论的力量，我们的目标是解决驾驶员分心驾驶这一关键问题，这是导致道路事故的一个重要因素。我们的策略采用了一种双方法：利用基于图的变换点检测来精确定位时间中的动作，同时结合视频大型语言模型（Video-LLM）进行精确分类活动。通过仔细的提示工程，我们定制了Video-LLM，使其能够熟练处理驾驶活动的细节，即使数据稀疏，也能确保分类效果。经优化后，我们的框架轻便且适用于消费级GPU，因此在实际场景中具有广泛的应用前景。我们对该方法在SynDD2数据集上的测试进行了严格的评估，这是一个复杂的驾驶员分心驾驶行为基准数据集，它在事件分类和事件检测方面都取得了令人满意的成绩，证明了DeepLocalization在准确识别不同驾驶员行为及其时间发生情况方面具有巨大的潜力。
URL
https://arxiv.org/abs/2404.12258
