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对于异常的认知体内学习,主动目标跟踪是异常.

Qihui Wu1, Jiahao Li1, Fuhui Zhou2

  • 1College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China.

Communications engineering
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PubMed
概括
此摘要是机器生成的。

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认知嵌入式学习 (CEL) 通过在正常跟踪和异常处理之间动态切换来增强主动对象跟踪 (AOT). 这种新的方法在复杂环境中显著提高了跟踪成功率和效率.

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:

  • 积极对象跟踪 (AOT) 在复杂场景中面临挑战,原因是当前深度学习和强化学习框架的局限性,包括高计算成本和糟糕的概括性.
  • 嵌入式智能 (EI) 显示了通过物理互动学习的希望,但在跟踪任务中与严重的异常作斗争.
  • 现有的方法缺乏稳定性和适应性,以适应现实世界中的AOT应用,以及不可预测的环境变化.

研究的目的:

  • 提出一种新的嵌入式学习方法,即认知嵌入式学习 (CEL),以解决现有的AOT框架的局限性,特别是处理复杂的场景和异常.
  • 开发一种能够在正常跟踪和异常处理模式之间动态切换以提高AOT性能的系统.
  • 引入一类客观函数,以减轻严重异常期间因函数不可测量和数据混而产生的问题.

主要方法:

  • 开发了认知体内学习 (CEL) 框架,灵感来自人类大脑的双重决策系统.
  • 集成的专用模块:异常认知,规则推理和异常消除,使动态模式切换成为可能.
  • 引入了分类客观函数来处理异常情况中的数据混和非可测量性.

主要成果:

  • 在模拟和现实场景中使用无人机 (UAV) 异常主动目标跟踪进行了广泛的实验,证明了CEL的卓越性能.

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  • 与最先进的方法相比,CEL的成功率提高了361.4%.
  • 该方法显示,任务完成效率提高了54.4%,突出显示了它在具有挑战性的环境中的有效性.

    • 结论:

    • 认知嵌入式学习 (CEL) 为复杂和异常环境中主动对象跟踪提供了强大的智能解决方案.
    • 动态切换功能和专用模块显著提高了跟踪精度和效率.
    • CEL有可能推进AOT研究,并为具有挑战性的应用程序提供更可靠的跟踪系统.