在自由移动的摄像头中通过全球运动补偿和局部空间信息融合来检测移动的物体
Zhongyu Chen1,2,3, Rong Zhao1,2,3, Xindong Guo1,2,3
1School of Computer Science and Technology, North University of China, Taiyuan 030051, China.
Sensors (Basel, Switzerland)
|May 11, 2024
概括
这项研究引入了一种用于自由移动的摄像机的新型运动物体检测 (MOD) 方法,提高了光线变化和遮蔽的复杂场景的精度. 该方法通过整合本地和全球视觉信息来增强前景/后景细分,以实现强大的物体检测.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 机器学习 机器学习
背景情况:
- 运动物体检测 (MOD) 对于自由移动的摄像机来说,由于照明变化,遮蔽和复杂的摄像机运动,具有挑战性.
- 现有的方法与局部不规则的变化和由动态场景引起的运动特征的全球不连续性作斗争.
研究的目的:
- 提出一种新的MOD方法,有效地利用本地和全球视觉信息进行前景/后景细分.
- 为了提高对象检测在具有挑战性的现实世界的场景中的准确性和稳定性.
主要方法:
- 优化相对的框架间转换,以绝对转换,参考中间,用于全球运动补偿.
- 使用空间变压器网络 (STN) 微调全球转换.
- 利用像素差异和局部空间变化一致性用于前景物体检测,结合光学流量细分.
主要成果:
- 与CDNET2014,FBMS-59和CBD数据集上的最先进的方法相比,检测准确度提高了1.5%.
- 在具有挑战性的场景中表现出显著的有效性,包括阴影,照明变化,摄像机动,遮蔽和移动的背景.
结论:
- 拟议的MOD方法有效地整合了本地和全球视觉信息,以实现强大的前景/后景细分.
- 这种方法在复杂和动态的环境中显著提高了检测准确度和可靠性,并且可以自由移动相机.
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