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首页> 外文会议>IEEE International Conference on Image Processing >Using C3D to Detect Rear Overtaking Behavior
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Using C3D to Detect Rear Overtaking Behavior

机译：使用C3D检测后方超车行为

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摘要

Avoiding traffic accidents is critical since the death of traffic accidents is the eighth among the top ten leading causes of death in 2018. This paper proposes a light-weight convolutional 3D (C3D) network with five 3D convolution layers and two fully-connected layers to predict overtaking behavior. This network utilizes the last layer of convolution layer to learn the overtaking object location in the final frame. Based on NVIDIA Jetson TX2, the proposed C3D network achieves 91.46% accuracy to detect overtaking behavior on rainy days. To generate this excellent deep learning model, we use an efficient labeling tool, called ezLabel, which is a free SaaS for academia group with 96,000 opened image data samples for deep learning. ezLabel owns outstanding route prediction and fitting functions, which speeds up with the factor of ten compared to traditional tools. Users only label the object in its first frame and in its final frame, and then ezLabel labels the object in all frames in between and fits the bounding box to the object. The ezLabel can be used to label objects captured with any moving or static cameras efficiently.
机译：避免交通事故至关重要，因为交通事故的死亡是2018年十大主要死亡原因中的第八位。本文提出了一种轻量级的卷积3D（C3D）网络，该网络具有五个3D卷积层和两个全连接层预测超车行为。该网络利用卷积层的最后一层来了解最终帧中的超车对象位置。基于NVIDIA Jetson TX2，建议的C3D网络达到91.46％的准确性，可在雨天检测超车行为。为了生成这种出色的深度学习模型，我们使用了一种称为ezLabel的高效标记工具，该工具是针对学术界的免费SaaS，具有96,000个用于深度学习的已打开图像数据样本。 ezLabel拥有出色的路线预测和拟合功能，与传统工具相比，其速度提高了十倍。用户仅在其第一帧和最后一帧中标记对象，然后ezLabel在这两个帧之间的所有帧中标记该对象，并使边界框适合该对象。 ezLabel可以有效地标记使用任何移动或静态摄像机捕获的对象。

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