A semantic understanding of road traffic can help people understand road traffic flow
situations and emergencies more accurately and provide a more accurate basis for anomaly …

Understanding human behavior and activity facilitates advancement of numerous real-world
applications, and is critical for video analysis. Despite the progress of action recognition …

Human activity recognition (HAR) is a complex and multifaceted problem. The research
community has reported numerous approaches to perform HAR. Along with HAR …

An integral part of video analysis and surveillance is temporal activity detection, which
means to simultaneously recognize and localize activities in long untrimmed videos …

Derived from rapid advances in computer vision and machine learning, video analysis tasks
have been moving from inferring the present state to predicting the future state. Vision-based …

Most existing forecasting systems are memory-based methods, which attempt to mimic
human forecasting ability by employing various memory mechanisms and have progressed …

We propose to predict the future trajectories of observed agents (eg, pedestrians or vehicles)
by estimating and using their goals at multiple time scales. We argue that the goal of a …

Abstract We present Long Short-term TRansformer (LSTR), a temporal modeling algorithm
for online action detection, which employs a long-and short-term memory mechanism to …

Most recent approaches for online action detection tend to apply Recurrent Neural Network
(RNN) to capture long-range temporal structure. However, RNN suffers from non-parallelism …

Weakly supervised anomaly detection is a challenging task since frame-level labels are not
given in the training phase. Previous studies generally employ neural networks to learn …