This article summarizes research trends on the topic of anomaly detection in video feeds of a
single scene. We discuss the various problem formulations, publicly available datasets and …

Computer vision has evolved in the last decade as a key technology for numerous
applications replacing human supervision. Timely detection of traffic violations and …

Human trajectory forecasting with multiple socially interacting agents is of critical importance
for autonomous navigation in human environments, eg, for self-driving cars and social …

As a vital topic in media content interpretation, video anomaly detection (VAD) has made
fruitful progress via deep neural network (DNN). However, existing methods usually follow a …

Video anomaly detection is of critical practical importance to a variety of real applications
because it allows human attention to be focused on events that are likely to be of interest, in …

Social interaction is an important topic in human trajectory prediction to generate plausible
paths. In this paper, we present a novel insight of group-based social interaction model to …

We present a novel end-to-end partially supervised deep learning approach for video
anomaly detection and localization using only normal samples. The insight that motivates …

Time-efficient anomaly detection and localization in video surveillance still remains
challenging due to the complexity of “anomaly”. In this paper, we propose a cuboid-patch …

Video anomaly detection is a challenging task owing to the rare and diverse nature of
abnormal events. However, most of the existing methods only learn the normality in a single …

Progress in video anomaly detection research is currently slowed by small datasets that lack
a wide variety of activities as well as flawed evaluation criteria. This paper aims to help move …