IEEE Trans Pattern Anal Mach Intell. 2017 May;39(5):981-994. doi: 10.1109/TPAMI.2016.2560807. Epub 2016 Apr 29.
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 L 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.
在本文中，我们解决了静止人群分析的问题，这与建模人群场景中的移动群体一样重要，并在人群监控中找到了许多重要的应用。我们的主要贡献是提出了一种稳健的算法来估计前景像素静止的时间。这比仅仅减去背景要困难得多，因为由于物体的局部运动、光照变化和遮挡，单个帧的失败可能导致静止时间估计的大误差。为了实现稳健和准确的估计，通过混合偏导数（二阶梯度）在空间和时间维度上共同添加稀疏约束，形成一个 3D 静止时间图。它被公式化为一个 L 优化问题。除了背景减法之外，它还通过使用局部共享的前景点码本来区分在时空空间中接近或重叠的不同前景对象。所提出的技术通过三个应用进一步得到证明。1）基于静止时间估计的结果，提出了 12 个描述符来检测四种类型的静止人群活动。2）估计平均静止时间图以分析人群场景结构。3）静止时间估计的结果也用于研究静止人群组对交通模式的影响。
