Video Processing and Understanding Lab, Universidad Autónoma de Madrid, 28049 Madrid, Spain.
Sensors (Basel). 2018 Dec 5;18(12):4290. doi: 10.3390/s18124290.
During the last few years, abandoned object detection has emerged as a hot topic in the video-surveillance community. As a consequence, a myriad of systems has been proposed for automatic monitoring of public and private places, while addressing several challenges affecting detection performance. Due to the complexity of these systems, researchers often address independently the different analysis stages such as foreground segmentation, stationary object detection, and abandonment validation. Despite the improvements achieved for each stage, the advances are rarely applied to the full pipeline, and therefore, the impact of each stage of improvement on the overall system performance has not been studied. In this paper, we formalize the framework employed by systems for abandoned object detection and provide an extensive review of state-of-the-art approaches for each stage. We also build a multi-configuration system allowing one to select a range of alternatives for each stage with the objective of determining the combination achieving the best performance. This multi-configuration is made available online to the research community. We perform an extensive evaluation by gathering a heterogeneous dataset from existing data. Such a dataset allows considering multiple and different scenarios, whereas presenting various challenges such as illumination changes, shadows, and a high density of moving objects, unlike existing literature focusing on a few sequences. The experimental results identify the most effective configurations and highlight design choices favoring robustness to errors. Moreover, we validated such an optimal configuration on additional datasets not previously considered. We conclude the paper by discussing open research challenges arising from the experimental comparison.
在过去的几年中，废弃物体检测已成为视频监控领域的热门话题。因此，已经提出了许多系统用于自动监控公共场所和私人场所，同时解决影响检测性能的几个挑战。由于这些系统的复杂性，研究人员通常独立地解决不同的分析阶段，如前景分割、静止物体检测和废弃验证。尽管每个阶段都取得了改进，但这些进展很少应用于整个管道，因此，改进的每个阶段对整个系统性能的影响尚未得到研究。在本文中，我们正式确定了用于废弃物体检测的系统所采用的框架，并对每个阶段的最新方法进行了广泛的回顾。我们还构建了一个多配置系统，允许为每个阶段选择一系列替代方案，目的是确定实现最佳性能的组合。该多配置可供研究社区在线使用。我们通过从现有数据中收集异构数据集来进行广泛评估。与现有文献集中在少数几个序列上不同，这样的数据集允许考虑多个不同的场景，同时呈现各种挑战，如光照变化、阴影和高密度的移动物体。实验结果确定了最有效的配置，并突出了有利于容错性的设计选择。此外，我们还在以前未考虑的其他数据集上验证了这种最佳配置。最后，我们通过讨论实验比较中出现的开放性研究挑战来结束本文。
