IEEE Trans Image Process. 2017 Mar;26(3):1127-1142. doi: 10.1109/TIP.2016.2642779. Epub 2016 Dec 21.
There is a huge proliferation of surveillance systems that require strategies for detecting different kinds of stationary foreground objects (e.g., unattended packages or illegally parked vehicles). As these strategies must be able to detect foreground objects remaining static in crowd scenarios, regardless of how long they have not been moving, several algorithms for detecting different kinds of such foreground objects have been developed over the last decades. This paper presents an efficient and high-quality strategy to detect stationary foreground objects, which is able to detect not only completely static objects but also partially static ones. Three parallel nonparametric detectors with different absorption rates are used to detect currently moving foreground objects, short-term stationary foreground objects, and long-term stationary foreground objects. The results of the detectors are fed into a novel finite state machine that classifies the pixels among background, moving foreground objects, stationary foreground objects, occluded stationary foreground objects, and uncovered background. Results show that the proposed detection strategy is not only able to achieve high quality in several challenging situations but it also improves upon previous strategies.
存在大量的监控系统，这些系统需要能够检测不同类型的静止前景目标（例如，无人看管的包裹或非法停放的车辆）的策略。由于这些策略必须能够检测到在人群场景中保持静止的前景对象，而不管它们已经静止了多长时间，因此在过去几十年中已经开发出了用于检测不同类型的此类前景对象的几种算法。本文提出了一种高效、高质量的检测静止前景对象的策略，该策略不仅能够检测完全静止的对象，还能够检测部分静止的对象。使用三个具有不同吸收率的并行非参数检测器来检测当前移动的前景对象、短期静止的前景对象和长期静止的前景对象。检测器的结果被输入到一个新颖的有限状态机中，该有限状态机将像素分类为背景、移动的前景对象、静止的前景对象、被遮挡的静止前景对象和未被遮挡的背景。结果表明，所提出的检测策略不仅能够在多种具有挑战性的情况下实现高质量，而且还优于以前的策略。
