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首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >L0 Regularized Stationary-Time Estimation for Crowd Analysis
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L0 Regularized Stationary-Time Estimation for Crowd Analysis

机译：L0正则平稳时间估计用于人群分析

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摘要

In this paper, we tackle the problem of stationary crowd analysis which is as important as modeling mobile groups in crowd scenes and finds many important applications in crowd surveillance. Our key contribution is to propose a robust algorithm for estimating how long a foreground pixel becomes stationary. It is much more challenging than only subtracting background because failure at a single frame due to local movement of objects, lighting variation, and occlusion could lead to large errors on stationary-time estimation. To achieve robust and accurate estimation, sparse constraints along spatial and temporal dimensions are jointly added by mixed partials (which are second-order gradients) to shape a 3D stationary-time map. It is formulated as an L0 optimization problem. Besides background subtraction, it distinguishes among different foreground objects, which are close or overlapped in the spatio-temporal space by using a locally shared foreground codebook. The proposed technologies are further demonstrated through three applications. 1) Based on the results of stationary-time estimation, 12 descriptors are proposed to detect four types of stationary crowd activities. 2) The averaged stationary-time map is estimated to analyze crowd scene structures. 3) The result of stationary-time estimation is also used to study the influence of stationary crowd groups to traffic patterns.
机译：在本文中，我们解决了固定人群分析的问题，该问题与在人群场景中对移动组进行建模一样重要，并且在人群监视中发现了许多重要的应用。我们的主要贡献是提出一种鲁棒的算法，用于估计前景像素变得静止的时间。它比仅减去背景更具挑战性，因为由于对象的局部移动，光照变化和遮挡而导致的单帧故障可能会导致静止时间估计上的大误差。为了实现鲁棒且准确的估计，沿着空间和时间维度的稀疏约束由混合部分（它们是二阶梯度）共同添加，以形成3D固定时间地图。它被公式化为L0优化问题。除了背景减法外，它还使用本地共享的前景代码本来区分在时空空间中接近或重叠的不同前景对象。所提出的技术将通过三个应用程序进行进一步演示。 1）根据固定时间估计的结果，提出了12个描述符来检测四种类型的固定人群活动。 2）估计平均静止时间图以分析人群场景结构。 3）静态时间估计的结果还用于研究静态人群对交通方式的影响。

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