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DeepLocalization: Using change point detection for Temporal Action Localization
April 19, 2024, 4:45 a.m. | Mohammed Shaiqur Rahman, Ibne Farabi Shihab, Lynna Chu, Anuj Sharma
cs.CV updates on arXiv.org arxiv.org
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 …
abstract advanced arxiv behavior change cs.cv deep learning detection driver driving framework issue localization monitoring power real-time study temporal type
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