Anomaly Detection in Surveillance Video: An Unsupervised Unity Encoder Approach

Enhancing video anomaly detection through dual-path reconstruction and diverse pseudo-anomaly generation

This paper introduces a VAD method leveraging a multi-source pseudo-anomaly generator and a dual-decoder framework that integrates spatiotemporal perturbations and object-level local appearance perturbations to enhance anomaly coverage.

Reconstructive Visual Tuning for Weakly Supervised Video Anomaly Detection

Weakly supervised video anomaly detection (WS-VAD) presents a significant challenge in security video surveillance, as it aims to accurately identify anomaly frames in untrimmed videos with only video-level supervision. Several recent studies exploit vision-language pre-training models, e.g., CLIP, to take advantage of...

Explainable Video Anomaly Detection Using Video Masked Autoencoders and Convolutional LSTM

This paper presents a unified and interpretable framework for video anomaly detection that integrates a pretrained Video Masked Autoencoder (VideoMAE), a ConvLSTM-based temporal modeling module, and a 3D CNN decoder for spatiotemporal reconstruction. The model is trained exclusively on normal video sequences and identi...

Detection and Identification of Anomalous Events in Videos Using Deep Learning-Based Anomaly Detection

STC-VAD: spatio-temporal collaborative weakly-supervised video anomaly detection

Weakly supervised video anomaly detection is of great importance to security surveillance. The task detects anomaly with solely video-level labels. Despite significant progress made by existing methods, they have the following limitations: On one hand, they pay little attention to the specific spatial regions where ano...

Spatio-temporal video anomaly detection via CNN-ViT autoencoder and farneback optical flow

This research introduces a novel memory-augmented CNN-ConvViT autoencoder framework for unsupervised video anomaly detection and introduces a Temporal-Aware Prototype Memory Module (TAPMM) that explicitly learns normal spatio-temporal behavior patterns.