无人机跟踪人群:一个新的数据集
Mohamad Alansari1, Oussama Abdul Hay2,3, Sara Alansari4
1Department of Computer Science, Khalifa University, Abu Dhabi, UAE. 100061914@ku.ac.ae.
Scientific data
|January 3, 2024
概括
从无人机上追踪人群中穿着类似衣服的人很难. 我们创建了D-PTUAC数据集,以帮助开发更好的无人机追踪算法,以对外观均的人群进行追踪.
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:
- 无人机在统一外观的人群中跟踪人员,这带来了重大挑战.
- 区分具有相似服装的个体和处理多尺度变异是关键的困难.
研究的目的:
- 引入一个新的数据集,D-PTUAC (无人机人员追踪统一外观人群),以应对这些跟踪挑战.
- 促进先进无人机跟踪算法的开发和评估.
主要方法:
- D-PTUAC数据集包括138个序列,超过121,000个.
- 每个都有手动注释,有边界框和18个具有挑战性的属性,涵盖了不同的观点和场景复杂度.
- 使用44个最先进的 (SOTA) 追踪器进行了广泛的实验.
主要成果:
- 数据集的全面注释使各种具有挑战性的属性进行性能分析.
- 实验显示,与其他数据相比,这个数据集上的现有视觉对象追踪器的性能差距较大.
- 这凸显了当前跟踪器在空中跟踪场景中的局限性.
结论:
- D-PTUAC数据集对于推进无人机追踪人员研究在外表均的人群中至关重要.
- 显然需要专门的端到端的空中视觉物体追踪器,考虑到空中环境的独特特性.
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