自动设定视觉定位阈值，提升不同环境下的识别鲁棒性。
Adaptive Thresholding for Visual Place Recognition using Negative Gaussian Mixture Statistics
- 利用非目标场景的高斯混合统计特性自动生成匹配阈值
- 在多个数据集上实现稳定性能，无需人工调参
- 适合需要部署于多变环境的机器人导航系统
视觉定位（VPR）是基于摄像头的地图构建与导航中的关键技术。由于季节变化、光照、环境结构改变及临时人流车流等因素，同一地点的图像可能差异显著，带来识别挑战。现有研究通常通过召回率@K和ROC曲线评估图像描述子性能，但在实际机器人应用中，判断匹配是否有效常依赖手动设定的阈值，且难以适应多样视觉场景。本文提出一种基于‘负样本’高斯混合统计的自适应阈值方法，通过分析非目标位置的图像特征分布，自动确定合适匹配阈值。实验表明，该方法可在多个图像数据库和不同描述子下稳定工作，显著减少人工干预。
原文摘要 · Abstract (English)
Visual place recognition (VPR) is an important component technology for camera-based mapping and navigation applications. This is a challenging problem because images of the same place may appear quite different for reasons including seasonal changes, weather illumination, structural changes to the environment, as well as transient pedestrian or vehicle traffic. Papers focusing on generating image descriptors for VPR report their results using metrics such as recall@K and ROC curves. However, for a robot implementation, determining which matches are sufficiently good is often reduced to a manually set threshold. And it is difficult to manually select a threshold that will work for a variety of visual scenarios. This paper addresses the problem of automatically selecting a threshold for VPR by looking at the 'negative' Gaussian mixture statistics for a place - image statistics indicating not this place. We show that this approach can be used to select thresholds that work well for a variety of image databases and image descriptors.
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