论文标题
基于目标跟踪中深度学习的数据融合算法的研究
Research on Data Fusion Algorithm Based on Deep Learning in Target Tracking
论文作者
论文摘要
目的是针对限制，深层长期记忆网络（DLSTM）算法无法执行并行计算，并且无法获得全球信息，在本文中，在本文中，特征提取和特征处理是根据眼睛运动数据和跟踪数据的特征进行的，然后通过将卷积型网络（CNN）引入一个新的网络from网络，并通过将新的网络from网络介绍到一个新的范围内，并且提出了基于长期和短期内存网络的算法。实验结果表明，与基于深度学习的两种融合算法相比，本文提出的算法在融合质量方面表现良好。
Aiming at the limitation that deep long and short-term memory network(DLSTM) algorithm cannot perform parallel computing and cannot obtain global information, in this paper, feature extraction and feature processing are firstly carried out according to the characteristics of eye movement data and tracking data, then by introducing a convolutional neural network (CNN) into a deep long and short-term memory network, developed a new network structure and designed a fusion strategy, an eye tracking data fusion algorithm based on long and short-term memory network is proposed. The experimental results show that compared with the two fusion algorithms based on deep learning, the algorithm proposed in this paper performs well in terms of fusion quality.
