Abstract
Crowd behavior understanding is recognized as a complex problem due to unpredictable behavior of humans and complex interactions of individuals in groups. For crowd managers, it is crucial to understand the crowd dynamics to manage the crowd efficiently and effectively. Current practice of crowd management is based on manual analysis of the scene. Such manual analysis of the scene is a tedious job and usually prone to errors due to limited human capabilities. Therefore, the task of automatizing crowd analysis has received tremendous attention from the research community during the recent years. In this paper, we propose a deep model framework that automatically characterizes different crowd behaviors based on motion and appearance. We first extract dense trajectories from the input video segment and then generate trajectory image by projecting trajectories on to image plane. Trajectory image effectively captures relative motion in the scene. We use stack of trajectory images to train deep convolutional network that learns compact and powerful representation of motion in the scene. We evaluate our approach on UCF, CUHK, and Crowd-11 benchmark datasets. From the experiment results, we demonstrate, both in quantitative and qualitative ways, that the proposed framework outperforms other existing methods by a great margin.