تقرير
Deep Fusion of Multi-Object Densities Using Transformer
| العنوان: | Deep Fusion of Multi-Object Densities Using Transformer |
|---|---|
| المؤلفون: | Li, Lechi, Dai, Chen, Xia, Yuxuan, Svensson, Lennart |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning |
| الوصف: | In this paper, we demonstrate that deep learning based method can be used to fuse multi-object densities. Given a scenario with several sensors with possibly different field-of-views, tracking is performed locally in each sensor by a tracker, which produces random finite set multi-object densities. To fuse outputs from different trackers, we adapt a recently proposed transformer-based multi-object tracker, where the fusion result is a global multi-object density, describing the set of all alive objects at the current time. We compare the performance of the transformer-based fusion method with a well-performing model-based Bayesian fusion method in several simulated scenarios with different parameter settings using synthetic data. The simulation results show that the transformer-based fusion method outperforms the model-based Bayesian method in our experimental scenarios. Comment: Accepted for publication in ICASSP 2023. Python implementation is available at https://github.com/Lechili/DeepFusion |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2209.08857 |
| رقم الأكسشن: | edsarx.2209.08857 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |