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

arXiv: https://arxiv.org/abs/2211.12776 · HTML: https://mlsanity.com/paper/2211.12776/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 23 Nov 2022.

## Builds on (most cited first, 3 of 3)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing](https://mlsanity.com/paper/1611.01942/) (2016)
- [Concurrent Activity Recognition with Multimodal CNN-LSTM Structure](https://mlsanity.com/paper/1702.01638/) (2017)

## Abstract (arXiv)

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.