Pedestrian action prediction has been a topic of active research in recent years resulting in
many new algorithmic solutions. However, measuring the overall progress towards solving …

Abstract Temporal Action Localization (TAL) has experienced remarkable success under the
supervised learning paradigm. However, existing TAL methods are rooted in the closed set …

Traffic accident anticipation aims to accurately and promptly predict the occurrence of a
future accident from dashcam videos, which is vital for a safety-guaranteed self-driving …

Learning spatial and temporal relations among people plays an important role in
recognizing group activity. Recently, transformer-based methods have become popular …

This paper presents a new relational network for group activity recognition. The essence of
the network is to integrate conditional random fields (CRFs) with self-attention to infer the …

Some group activities, such as team sports and choreographed dances, involve closely
coupled interaction between participants. Here we investigate the tasks of inferring and …

Group activity recognition is a challenging task because it involves diverse individual actions
and complex relations. Most existing methods enhance individual representation by …

Early action recognition (action prediction) from limited preliminary observations plays a
critical role for streaming vision systems that demand real-time inference, as video actions …

Group activity recognition aims to identify group activities from the videos. Most of the
previous methods focus on modeling between individuals (one-to-one), which ignores the …

This paper describes an energy-based learning method that predicts the activities of multiple
agents simultaneously. It aims to forecast both upcoming actions and paths of all agents in a …