Predicting the crowd behavior in complex environments is a key requirement for crowd and disaster management, architectural design, and urban planning.
We evaluate over CATER dataset and find that Hopper achieves 73. 2% Top-1 accuracy using just 1 FPS by hopping through just a few critical frames.
We present a floorplan embedding technique that uses an attributed graph to represent the geometric information as well as design semantics and behavioral features of the inhabitants as node and edge attributes.
Given its crucial role, there is a need to better understand and model the dynamics of GitHub as a social platform.
Current research on recommender systems mostly focuses on matching users with proper items based on user interests.
In this paper, we present a Hierarchical Information Diffusion (HID) framework by integrating user representation learning and multiscale modeling.
In this paper, we propose an approach to instantly predict the long-term flow of crowds in arbitrarily large, realistic environments.