Divide and Conquer in Video Anomaly Detection: A Comprehensive Review and New Approach
Video anomaly detection is a complex task, and the principle of "divide and conquer" is often regarded as an effective approach to tackling intricate issues. It's noteworthy that recent methods in video anomaly detection have revealed the application of the divide and conquer philosophy (albeit with distinct perspectives from traditional usage), yielding impressive outcomes. This paper systematically reviews these literatures from six dimensions, aiming to enhance the use of the divide and conquer strategy in video anomaly detection. Furthermore, based on the insights gained from this review, a novel approach is presented, which integrates human skeletal frameworks with video data analysis techniques. This method achieves state-of-the-art performance on the ShanghaiTech dataset, surpassing all existing advanced methods.
Code (1)
Tasks
Anomaly DetectionPhilosophyVideo Anomaly DetectionSimilar Papers 제목 키워드 기반
Temporal Divide-and-Conquer Anomaly Actions Localization in Semi-Supervised Videos with Hierarchical Transformer
Anomaly action detection and localization play an essential role in security and advanced surveillance systems. However, due to the tremendous amount of surveillance videos, most of the available data for the task is unl…
Action DetectionAnomaly DetectionAnomaly LocalizationMultiple Instance LearningDivide and Conquer: High-Resolution Industrial Anomaly Detection via Memory Efficient Tiled Ensemble
Industrial anomaly detection is an important task within computer vision with a wide range of practical use cases. The small size of anomalous regions in many real-world datasets necessitates processing the images at a h…
Anomaly DetectionGPUOmAgent: A Multi-modal Agent Framework for Complex Video Understanding with Task Divide-and-Conquer
Recent advancements in Large Language Models (LLMs) have expanded their capabilities to multimodal contexts, including comprehensive video understanding. However, processing extensive videos such as 24-hour CCTV footage …
AI AgentLarge Language ModelVideo UnderstandingDCARL: A Divide-and-Conquer Framework for Autoregressive Long-Trajectory Video Generation
Long-trajectory video generation is a crucial yet challenging task for world modeling primarily due to the limited scalability of existing video diffusion models (VDMs). Autoregressive models, while offering infinite rol…
Video GenerationA Divide-and-Conquer Method for Scalable Low-Rank Latent Matrix Pursuit
Data fusion, which effectively fuses multiple prediction lists from different kinds of features to obtain an accurate model, is a crucial component in various computer vision applications. Robust late fusion (RLF) is a r…
Event DetectionObject Categorization