Using C3D to Detect Rear Overtaking Behavior

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.