Crowd feature perception is an essential step for us to understand the crowd behavior. However, as the individuals present not only the sociality but also the randomness, there remain great challenges to extract the sociality of the individual directly. In this paper, we propose a crowd feature perception algorithm based on a sparse linear model (SLM). It builds the statistical characterization of the sociality by assuming a priori distribution of the SLM. First, we calculate the optical flow to extract the motion information of the crowd. Second, we input the video motion features to the sparse coding and generate the SLM. The super-Gaussian prior distributions in SLMs build the statistical characterization of the sociality. In addition, we combine the infinite Hidden Markov Model (iHMM) statistic model to determine whether the detected event is an abnormal event. We validate our method on UMN dataset and simulate dataset for abnormal detection, and the experiments show that this algorithm generates promising result compared with other state-of-art methods.

中文翻译：

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基于稀疏编码的人群异常事件检测

人群特征感知是我们了解人群行为的必要步骤。然而，由于个体不仅呈现社会性，而且呈现随机性，直接提取个体的社会性仍然存在很大挑战。在本文中，我们提出了一种基于稀疏线性模型（SLM）的人群特征感知算法。它通过假设 SLM 的先验分布来构建社会性的统计特征。首先，我们计算光流来提取人群的运动信息。其次，我们将视频运动特征输入到稀疏编码中并生成 SLM。SLM 中的超高斯先验分布构建了社会性的统计特征。此外，我们结合无限隐马尔可夫模型（iHMM）统计模型来判断检测到的事件是否为异常事件。我们在 UMN 数据集上验证了我们的方法，并模拟了异常检测数据集，实验表明，与其他最先进的方法相比，该算法产生了有希望的结果。