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arXiv · 2404.12258

DeepLocalization: Using change point detection for Temporal Action Localization

Mohammed Shaiqur Rahman·Ibne Farabi Shihab·Lynna Chu·Anuj Sharma
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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.

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Mohammed Shaiqur Rahman, Ibne Farabi Shihab, Lynna Chu, Anuj Sharma. 2024-04-18. DeepLocalization: Using change point detection for Temporal Action Localization. https://arxiv.org/abs/2404.12258

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