Research on Data Fusion Algorithm Based on Deep Learning in Target Tracking

TL;DR

This paper proposes a novel deep learning-based data fusion algorithm for eye tracking, integrating CNN with LSTM to improve global information extraction and parallel processing capabilities, resulting in enhanced fusion quality.

Contribution

It introduces a new network structure combining CNN and LSTM for eye tracking data fusion, addressing limitations of existing deep learning methods.

Findings

The proposed algorithm outperforms existing fusion algorithms in quality.

The new network effectively captures global information and enables parallel processing.

Experimental results validate the superiority of the method.

Abstract

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.