Research on Data Fusion Algorithm Based on Deep Learning in Target Tracking
This work addresses incremental improvements in target tracking for applications like eye movement analysis, focusing on enhancing data fusion quality.
The paper tackled the limitations of deep LSTM in target tracking by integrating CNN to enable parallel computing and capture global information, resulting in a new data fusion algorithm that outperforms two existing deep learning-based fusion methods in fusion quality.
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.