用运动轨迹图检测变道和超车，轻量高效适合车载部署。
An object detection approach for lane change and overtake detection from motion profiles
- 将行车视频转为运动轨迹图，用目标检测识别驾驶动作。
- 引入CoordConvolution提升性能，mAP和F1得分优于现有方法。
- 计算开销极低，适合在车载设备实时运行。
在车队管理与驾驶员监控领域，从行车记录仪视频中提取关键驾驶事件，同时减少存储与分析的数据量极具挑战。本文提出一种新型目标检测方法，应用于将驾驶视频压缩为单一图像的运动轨迹图，以识别变道与超车行为。为训练与测试模型，我们构建了一个内部数据集，包含来自异构行车记录仪视频的运动轨迹图，并由人工标注了车辆自身的变道与超车动作。除了标准目标检测框架外，本文还证明引入CoordConvolution层可显著提升模型性能，在mAP与F1分数上达到当前最优水平。所提方案计算开销极低，特别适合在车载设备上实时运行。
原文摘要 · Abstract (English)
In the application domain of fleet management and driver monitoring, it is very challenging to obtain relevant driving events and activities from dashcam footage while minimizing the amount of information stored and analyzed. In this paper, we address the identification of overtake and lane change maneuvers with a novel object detection approach applied to motion profiles, a compact representation of driving video footage into a single image. To train and test our model we created an internal dataset of motion profile images obtained from a heterogeneous set of dashcam videos, manually labeled with overtake and lane change maneuvers by the ego-vehicle. In addition to a standard object-detection approach, we show how the inclusion of CoordConvolution layers further improves the model performance, in terms of mAP and F1 score, yielding state-of-the art performance when compared to other baselines from the literature. The extremely low computational requirements of the proposed solution make it especially suitable to run in device.
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