Advancing Video Anomaly Detection: A Concise Review and a New Dataset

TL;DR

This paper provides a concise review of Video Anomaly Detection, emphasizing dataset quality and diversity, and introduces a new comprehensive dataset, MSAD, to advance research in diverse surveillance scenarios.

Contribution

It offers a thorough review of current VAD models and datasets, highlighting the importance of dataset quality, and introduces the MSAD dataset with diverse scenarios for improved model training.

Findings

MSAD dataset includes 14 diverse scenarios with challenging variations.

Recent models show improved performance when trained on MSAD.

The review identifies key challenges and future directions in VAD research.

Abstract

Video Anomaly Detection (VAD) finds widespread applications in security surveillance, traffic monitoring, industrial monitoring, and healthcare. Despite extensive research efforts, there remains a lack of concise reviews that provide insightful guidance for researchers. Such reviews would serve as quick references to grasp current challenges, research trends, and future directions. In this paper, we present such a review, examining models and datasets from various perspectives. We emphasize the critical relationship between model and dataset, where the quality and diversity of datasets profoundly influence model performance, and dataset development adapts to the evolving needs of emerging approaches. Our review identifies practical issues, including the absence of comprehensive datasets with diverse scenarios. To address this, we introduce a new dataset, Multi-Scenario Anomaly Detection (MSAD), comprising 14 distinct scenarios captured from various camera views. Our dataset has diverse motion patterns and challenging variations, such as different lighting and weather conditions, providing a robust foundation for training superior models. We conduct an in-depth analysis of recent representative models using MSAD and highlight its potential in addressing the challenges of detecting anomalies across diverse and evolving surveillance scenarios. [Project website: https://msad-dataset.github.io/]