Mamba meets tracker: exploiting token aggregation and diffusion for robust unmanned aerial vehicles tracking

IF 4.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Guocai Du, Peiyong Zhou, Nurbiya Yadikar, Alimjan Aysa, Kurban Ubul
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引用次数: 0

Abstract

The Transformer-based tracking approach achieves excellent results in unmanned aerial vehicles (UAV) tracking tasks. However, the existing tracking framework usually deals with this problem by visual grounding and visual tracking separately. This independent framework does not consider the correlation between the two steps mentioned above, that is, natural language description can provide global semantic information. Meanwhile, a separate framework is unable to conduct end-to-end training. As a remedy, We propose a joint natural language Mamba based tracking framework (named TADMT). Specifically, we propose a token aggregator that condenses rich features into a small number of visual tokens through a coarse to fine strategy to improve subsequent tracking speed. Then, we designed a mamba module based on the serpentine scanning strategy to effectively establish the relationship between natural language and visual images. In addition, we have designed a novel shift add multilayer perceptron in the prediction head, with the aim of achieving final classification and localization with less computation. Numerous experimental results have shown that TADMT achieves good tracking performance on six UAV tracking datasets and three general tracking datasets, with an average speed of 120FPS. The experimental results on the embedded platform also demonstrate the applicability of TADMT on UAV platforms.

曼巴满足跟踪器:利用令牌聚合和扩散鲁棒无人机跟踪
基于变压器的跟踪方法在无人机跟踪任务中取得了优异的效果。然而,现有的跟踪框架通常将视觉接地和视觉跟踪分开处理。这个独立的框架没有考虑上述两个步骤之间的相关性,即自然语言描述可以提供全局的语义信息。同时,单独的框架无法进行端到端的培训。为了解决这一问题,我们提出了一个基于自然语言曼巴语的联合跟踪框架(TADMT)。具体来说,我们提出了一个令牌聚合器,通过粗到细的策略将丰富的特征压缩成少量的视觉令牌,以提高后续的跟踪速度。然后,我们设计了一个基于蛇形扫描策略的曼巴模块,有效地建立了自然语言和视觉图像之间的关系。此外,我们在预测头部设计了一种新颖的移位叠加多层感知器,目的是用更少的计算量实现最终的分类和定位。大量实验结果表明,TADMT在6个无人机跟踪数据集和3个通用跟踪数据集上取得了良好的跟踪性能,平均速度为120FPS。在嵌入式平台上的实验结果也证明了TADMT在无人机平台上的适用性。
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来源期刊
Complex & Intelligent Systems
Complex & Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
9.60
自引率
10.30%
发文量
297
期刊介绍: Complex & Intelligent Systems aims to provide a forum for presenting and discussing novel approaches, tools and techniques meant for attaining a cross-fertilization between the broad fields of complex systems, computational simulation, and intelligent analytics and visualization. The transdisciplinary research that the journal focuses on will expand the boundaries of our understanding by investigating the principles and processes that underlie many of the most profound problems facing society today.
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