Abstract
Naturalistic driving action localization task aims to recognize and comprehend human behaviors and actions from video data captured during real-world driving scenarios. Previous studies have shown great action localization performance by applying a recognition model followed by probability-based post-processing. Nevertheless, the probabilities provided by the recognition model frequently contain confused information causing challenge for post-processing. In this work, we adopt an action recognition model based on self-supervise learning to detect distracted activities and give potential action probabilities. Subsequently, a constraint ensemble strategy takes advantages of multi-camera views to provide robust predictions. Finally, we introduce a conditional post-processing operation to locate distracted behaviours and action temporal boundaries precisely. Experimenting on test set A2, our method obtains the sixth position on the public leaderboard of track 3 of the 2024 AI City Challenge.
Abstract (translated)
自然驾驶行为定位任务旨在从真实世界驾驶场景中捕获的视频数据中识别和理解人类的行为和动作。先前的研究表明,通过应用一个识别模型并结合基于概率的后处理可以实现出色的动作定位性能。然而,识别模型提供的概率往往包含混淆信息,给后处理带来挑战。在这项工作中,我们采用了一个基于自监督学习的动作识别模型来检测分心活动并提供潜在的行为概率。随后,一种约束集成策略利用多摄像机视角的优势提供了稳健的预测。最后,我们引入了一种条件后处理操作以精确地定位分心行为和动作的时间边界。在测试集A2上的实验中,我们的方法在2024年AI城市挑战赛第3赛道的公共排行榜上获得了第六名的位置。
URL
https://arxiv.org/abs/2411.12525