Video object detection has great potential to enhance visual perception abilities for indoor mobile robots in various regions. In this paper, a novel memory mechanism is proposed to enhance the detection performance for moving sensor videos (MSV), which obtain from indoor mobile robot. And the proposed mechanism could be applied as an extension module for a number of existing image object detectors. First, we analyze characteristics of the indoor MSVs, concluding the key characteristics as mild changes, complicated contents and relative movements. Second, a memory-unit dispatching and application method is devised to maintain prior memory contents and utilize the contents to achieve better detection performance. Finally, we create a corresponding indoor MSV dataset and compress the mechanism into a module to evaluate its localization performance. Our experiment results are presented to illustrate the proposed mechanism and achieve an average localization margin by 19.8% compared with several representative original detectors.

视频对象检测在增强各个区域的室内移动机器人的视觉感知能力方面具有巨大潜力。本文提出了一种新颖的存储机制，以提高从室内移动机器人获得的运动传感器视频（MSV）的检测性能。所提出的机制可以用作许多现有图像对象检测器的扩展模块。首先，我们分析室内MSV的特性，总结出关键特性，如温和变化，复杂内容和相对运动。其次，设计了一种存储单元调度和应用方法，以维护先前的存储内容并利用该内容来实现更好的检测性能。最后，我们创建了一个相应的室内MSV数据集，并将该机制压缩到一个模块中以评估其定位性能。