对比两种基于背景图的静止目标检测方法，提升实时性与鲁棒性。
Comparison of Two Methods for Stationary Incident Detection Based on Background Image
- 用单背景和双背景（不同学习率）分别检测临时静止物体
- 在光照变化、部分遮挡下仍能准确追踪，支持实时运行
- 适合需要稳定检测静止目标的视频监控场景
常规背景减法用于视觉跟踪中的运动目标检测。本文采用基于背景减法的方案，检测临时静止物体，提出两种检测方法，并从检测性能与计算复杂度角度进行比较。第一种方法使用单一背景，第二种方法采用两个不同学习率生成的双背景，以检测短暂停止的物体。最后，通过归一化互相关（NCC）图像比对实现对检测到的静止物体在视频场景中的持续监测与追踪。所提方法对部分遮挡、短时完全遮挡及光照变化具有鲁棒性，且可实现实时处理。
原文摘要 · Abstract (English)
In general, background subtraction-based methods are used to detect moving objects in visual tracking applications. In this paper, we employed a background subtraction-based scheme to detect the temporarily stationary objects. We proposed two schemes for stationary object detection, and we compare those in terms of detection performance and computational complexity. In the first approach, we used a single background, and in the second approach, we used dual backgrounds, generated with different learning rates, in order to detect temporarily stopped objects. Finally, we used normalized cross correlation (NCC) based image comparison to monitor and track the detected stationary object in a video scene. The proposed method is robust with partial occlusion, short-time fully occlusion, and illumination changes, and it can operate in real time.
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