Traffic accident detection and anticipation is an obstinate road safety problem and
painstaking efforts have been devoted. With the rapid growth of video data, Vision-based …

Weakly supervised video anomaly detection (WSVAD) is a challenging task. Generating fine-
grained pseudo-labels based on weak-label and then self-training a classifier is currently a …

Video anomaly detection (VAD) aims to temporally locate abnormal events in a video.
Existing works mostly rely on training deep models to learn the distribution of normality with …

Video Anomaly Detection (VAD) finds widespread applications in security surveillance,
traffic monitoring, industrial monitoring, and healthcare. Despite extensive research efforts …

Video anomaly detection (VAD) without human monitoring is a complex computer vision task
that can have a positive impact on society if implemented successfully. While recent …

Abstract Weakly-supervised Video Anomaly Detection (wVAD) aims to detect frame-level
anomalies using only video-level labels in training. Due to the limitation of coarse-grained …

Open-set supervised anomaly detection (OSAD)-a recently emerging anomaly detection
area-aims at utilizing a few samples of anomaly classes seen during training to detect …

We tackle the complex problem of detecting and recognising anomalies in surveillance
videos at the frame level, utilising only video-level supervision. We introduce the novel …

Surveillance videos are important for public security. However current surveillance video
tasks mainly focus on classifying and localizing anomalous events. Existing methods are …

Video anomaly detection presents a significant challenge in computer vision, with the aim of
distinguishing various anomaly events from numerous normal ones. Weakly supervised …