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首页> 外文会议>Youth Academic Annual Conference of Chinese Association of Automation >Vehicle Behavior Recognition using Multi-Stream 3D Convolutional Neural Network
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Vehicle Behavior Recognition using Multi-Stream 3D Convolutional Neural Network

机译：使用多流3D卷积神经网络的车辆行为识别

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摘要

Vehicle behavior recognition take an essential part in the community of intelligent driving assistance systems. Recent approaches with 3D convolutional neural network (3DCNN) achieve reasonable recognition performance in laboratory settings. However, due to the complexity of network, the model is large and the reasoning is slow, so it is difficult to be applied in practice. In order to better handle the trade-off between size, precision and reasoning speed,a lightweight multi-stream 3DCNN model is proposed in this paper, which achieves fast reasoning speed and small model size while maintaining high precision. The 3DCNN model consists of three parts. Firstly, the module of SELayer-3DCNN is developed to extract appearance information from the RGB image sequence. The motion and edge information are also extracted from the optical flow sequence and edge image sequence, respectively. The edge information is applied to enhance the optical flow features. Secondly, a novel channel attention fusion strategy is proposed to improve the feature fusion and network ability. Finally, a 3D-RFB module is proposed to enhance the receptive field of the convolutional kernel. Furthermore, this paper presents a dataset of vehicle behavior. The advantages of the proposed method are verified by ablation experiments and comparative experiments while the real-time characteristics are maintained.
机译：车辆行为识别在智能驾驶辅助系统社区中的一个重要组成部分。最近与3D卷积神经网络（3DCNN）的方法在实验室设置中实现了合理的识别性能。然而，由于网络的复杂性，模型很大，推理速度很慢，因此难以在实践中应用。为了更好地处理尺寸，精度和推理速度之间的折衷，本文提出了一种轻量级的多流3DCNN模型，这在保持高精度的同时实现了快速推理速度和小型模型尺寸。 3DCNN模型由三个部分组成。首先，开发Selayer-3DCNN的模块以从RGB图像序列提取外观信息。运动和边缘信息分别从光学流序列和边缘图像序列中提取。应用边缘信息以增强光学流量特征。其次，提出了一种新的渠道注意融合策略来改善特征融合和网络能力。最后，提出了一种3D-RFB模块来增强卷积核的接受领域。此外，本文提出了车辆行为的数据集。通过消融实验和比较实验验证所提出的方法的优点，同时保持实时特征。

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