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可靠的物体跟踪通过多式混合特征提取和基于变压器的融合

Hongze Sun¹, Rui Liu¹, Wuque Cai¹

¹Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.

Neural networks : the official journal of the International Neural Network Society

|July 6, 2024

概括

这项研究引入了一种新型多式混合追踪器 (MMHT),用于可靠的视觉对象追踪. 通过整合事件数据来改进功能建模,MMHT有效地应对低光和杂乱等挑战.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
机器人技术机器人技术机器人技术

背景情况:

使用可见光的视觉对象跟踪面临着诸如低光,高动态范围和背景杂乱等挑战.
现有的多式联网方法经常使用本地特征交互,限制特征建模潜力.
可靠的对象跟踪需要强大的特征提取和融合在不同的视觉线索.

研究的目的:

提出一种新的多式混合追踪器 (MMHT),用于强大的单个对象追踪.
通过整合框架事件数据和先进的神经网络来增强特征建模.
为了提高复杂的视觉环境中的跟踪性能.

主要方法:

使用混合骨干,结合人工神经网络 (ANN) 和尖端神经网络 (SNN) 来进行特征提取.
采用统一编码器来对准不同视觉模式的特征.
开发了一种增强的基于变压器的模块,具有用于多式联接特征融合的注意力机制.

主要成果:

MMHT模型有效地构建了一个多尺度和多维的视觉特征空间.
实现了歧视性特征建模,从而提高了跟踪精度.
在广泛的实验中证明了与最先进的方法相比具有竞争力的性能.

结论:

拟议的MMHT模型有效地解决了视觉对象跟踪方面的挑战.
将事件数据与混合ANN-SNN架构集成,可以提高跟踪可靠性.
该研究强调了多式联络融合在强大的计算机视觉任务中的潜力.

关键词:

多式联络融合是多式联络的融合.对象跟踪,是指对象的跟踪.尖的神经网络的神经网络.变压器变压器变压器

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