基于长短期记忆的车辆行为动态识别网络

Vehicle behavior dynamic recognition network based on long short-term memory

高级辅助驾驶装置采用机器视觉技术实时处理摄录的行车前方车辆视频,动态识别并预估其姿态和行为。针对该类识别算法精度低、延迟大的问题,提出一种基于长短期记忆(LSTM)的车辆行为动态识别深度学习算法。首先,提取车辆行为视频中的关键帧;其次,引入双卷积网络并行对关键帧的特征信息进行分析,再利用LSTM网络对提取出的特性信息进行序列建模;最后,通过输出的预测得分判断出车辆行为类别。实验结果表明,所提算法识别准确率可达95.6%,对于单个视频的识别时间只要1.72 s;基于自建数据集,改进的双卷积算法相比普通卷积网络在准确率上提高8.02%,与传统车辆行为识别算法相比准确率提高6.36%。其中改进的双卷积网络算法相比普通卷积网络在准确率上提高8.02%,基于本文摘要中不能出现“本文”字样,请调整语句描述。英文摘要处作相应修改。请参照现在的改过的PDF文档英文摘要进行修改,而不是原修改稿数据集,与传统车辆行为识别算法相比准确率提高6.36%。同基于本文数据集的传统车辆行为识别算法相比准确率提高6.36%。

In the advanced assisted driving device,machine vision technology was used to process the video of vehicles in front in real time to dynamically recognize and predict the posture and behavior of vehicle. Concerning low precision and large delay of this kind of recognition algorithm,a deep learning algorithm for vehicle behavior dynamic recognition based on Long Short-Term Memory (LSTM) was proposed. Firstly,the key frames in vehicle behavior video were extracted. Secondly,a dual convolutional network was introduced to analyze the feature information of key frames in parallel,and then LSTM network was used to sequence the extracted characteristic information. Finally,the output predicted score was used to determine the behavior type of vehicle. The experimental results show that the proposed algorithm has an accuracy of 95.6%,and the recognition time of a single video is only 1.72 s. The improved dual convolutional network algorithm improves the accuracy by 8.02% compared with ordinary convolutional network and increases by 6.36% compared with traditional vehicle behavior recognition algorithm based on a self-built dataset.