Generalized video anomaly event detection: Systematic taxonomy and comparison of deep models
Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance
systems, enabling the temporal or spatial identification of anomalous events within videos …
systems, enabling the temporal or spatial identification of anomalous events within videos …
Amp-net: Appearance-motion prototype network assisted automatic video anomaly detection system
As essential tools for industry safety protection, automatic video anomaly detection systems
(AVADS) are designed to detect anomalous events of concern in surveillance videos …
(AVADS) are designed to detect anomalous events of concern in surveillance videos …
Improving generalization in visual reinforcement learning via conflict-aware gradient agreement augmentation
Learning a policy with great generalization to unseen environments remains challenging but
critical in visual reinforcement learning. Despite the success of augmentation combination in …
critical in visual reinforcement learning. Despite the success of augmentation combination in …
Deep learning for video anomaly detection: A review
Video anomaly detection (VAD) aims to discover behaviors or events deviating from the
normality in videos. As a long-standing task in the field of computer vision, VAD has …
normality in videos. As a long-standing task in the field of computer vision, VAD has …
DSDCLA: Driving style detection via hybrid CNN-LSTM with multi-level attention fusion
Driving style detection is an essential real-world requirement in diverse contexts, such as
traffic safety, car insurance and fuel consumption optimization. However, the existing …
traffic safety, car insurance and fuel consumption optimization. However, the existing …
Stochastic video normality network for abnormal event detection in surveillance videos
Abstract Video Anomaly Detection (VAD) aims to automatically identify unexpected spatial–
temporal patterns to detect abnormal events in surveillance videos. Existing unsupervised …
temporal patterns to detect abnormal events in surveillance videos. Existing unsupervised …
Learning appearance-motion normality for video anomaly detection
Video anomaly detection is a challenging task in the Computer vision community. Most
single task-based methods do not consider the independence of unique spatial and …
single task-based methods do not consider the independence of unique spatial and …
Learning causality-inspired representation consistency for video anomaly detection
Video anomaly detection is an essential yet challenging task in the multimedia community,
with promising applications in smart cities and secure communities. Existing methods …
with promising applications in smart cities and secure communities. Existing methods …
Distributional and spatial-temporal robust representation learning for transportation activity recognition
Transportation activity recognition (TAR) provides valuable support for intelligent
transportation applications, such as urban transportation planning, driving behavior …
transportation applications, such as urban transportation planning, driving behavior …
Adversarial contrastive distillation with adaptive denoising
Adversarial Robustness Distillation (ARD) is a novel method to boost the robustness of small
models. Unlike general adversarial training, its robust knowledge transfer can be less easily …
models. Unlike general adversarial training, its robust knowledge transfer can be less easily …