IEEE Trans Image Process. 2016 Sep;25(9):4354-4368. doi: 10.1109/TIP.2016.2590322. Epub 2016 Jul 11.
Pedestrian behavior modeling and analysis is important for crowd scene understanding and has various applications in video surveillance. Stationary crowd groups are a key factor influencing pedestrian walking patterns but was mostly ignored in the literature. It plays different roles for different pedestrians in a crowded scene and can change over time. In this paper, a novel model is proposed to model pedestrian behaviors by incorporating stationary crowd groups as a key component. Through inference on the interactions between stationary crowd groups and pedestrians, our model can be used to investigate pedestrian behaviors. The effectiveness of the proposed model is demonstrated through multiple applications, including walking path prediction, destination prediction, personality attribute classification, and abnormal event detection. To evaluate our model, two large pedestrian walking route datasets are built. The walking routes of around 15 000 pedestrians from two crowd surveillance videos are manually annotated. The datasets will be released to the public and benefit future research on pedestrian behavior analysis and crowd scene understanding.
行人行为建模与分析对于理解人群场景至关重要，并且在视频监控中有多种应用。静止的人群群体是影响行人行走模式的关键因素，但在文献中大多被忽视。它在拥挤场景中对不同行人起着不同作用，并且会随时间变化。本文提出了一种新颖的模型，通过将静止人群群体作为关键组成部分来对行人行为进行建模。通过推断静止人群群体与行人之间的相互作用，我们的模型可用于研究行人行为。所提模型的有效性通过多种应用得到了证明，包括行走路径预测、目的地预测、性格属性分类以及异常事件检测。为了评估我们的模型，构建了两个大型行人行走路线数据集。对来自两个群体监控视频的约15000名行人的行走路线进行了人工标注。这些数据集将向公众发布，以惠及未来关于行人行为分析和人群场景理解的研究。
