A Markov Random Walk Model for Loitering People Detection
A Markov Random Walk Model for Loitering People Detection
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DOI：
10.1109/iihmsp.2010.172
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发表时间：
2010-10
期刊：
影响因子：
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通讯作者：
Thi Thi Zin-Thi;P. Tin;T. Toriu;H. Hama
中科院分区：
文献类型：
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作者：
Thi Thi Zin-Thi;P. Tin;T. Toriu;H. Hama
如今，视频监控系统被广泛应用于火车站或机场等公共场所，以增强安全性。为了观察大型和复杂的设施，需要大量的摄像机。这些都产生了大量需要分析的数据。因此，至关重要的是支持人类安全工作人员使用自动监控应用程序，如果检测到与安全相关的事件，该应用程序将创建警报。通过这种方式，视频监控可以用来防止潜在的危险情况，而不仅仅是被用作法医工具，在事件发生后进行分析。在本文中，我们提出了一种支持人工操作的监控系统，通过自动检测游荡的人。通常情况下，游荡的人类行为往往会导致异常情况，如可疑的毒品交易活动、银行抢劫和扒窃等。因此，基于二维马尔可夫随机游走，研究了涉及多个对象的图像序列中的游荡检测问题，其中使用了描述不同数量的对象及其进出的运动和外观特征。为了获得高效和紧凑的表示，我们将弹道间上下文的时空信息编码到马尔可夫随机游走的转移矩阵中，然后提取其平稳分布和越界概率作为最终检测准则。该模型还通过积分出它们对观察概率的影响来降低对遮挡感兴趣区域的非感兴趣对象的敏感度。所得到的系统在真实数据集场景上进行了测试，给出了95%的性能结果。
Today video surveillance systems are widely used in public spaces, such as train stations or airports, to enhance security. In order to observe large and complex facilities a huge amount of cameras is required. These create a massive amount of data to be analyzed. It is therefore crucial to support human security staff with automatic surveillance applications, which will create an alert if security relevant events are detected. This way video surveillance could be used to prevent potentially dangerous situations, instead of just being used as forensic instrument, to analyze an event after it happened. In this treatise we present a surveillance system which supports human operators, by automatically detecting loitering people. Usually, loitering human behavior often leads to abnormal situations, like suspected drug-dealing activity, bank robbery, and pickpocket, etc. Thus, the problem of loitering detection in image sequences involving situations with multiple objects is studied based two dimensional Markov random walks in which both motion and appearance features describing the movements of a varying number of objects as well as their entries and exits are used. To obtain efficient and compact representations we encode the spatiotemporal information of intra-inter trajectory contexts into the transition matrix of a Markov Random Walk, and then extract its stationary distribution and boundary crossing probabilities as final detection criteria. The model is also made less sensitive to uninteresting objects occluding the region of interest by integration out their effect on the observation probabilities. The resulting system is tested on the real life dataset scenarios giving 95% performance results.