Anomaly Detection Using Computer Vision

Abstract

Anomaly detection using computer vision has gained significant importance in diverse domains, including surveillance, quality control, and cybersecurity. This paper presents a comprehensive investigation into anomaly detection using computer vision, covering principles, methodologies, applications, evaluation, and future directions. The paper highlights the challenges of detecting abnormal patterns and behaviors, emphasizing the potential of computer vision-based methods over traditional approaches. It explores feature extraction techniques, machine learning algorithms, and the role of transfer learning and generative models. Applications in video surveillance, quality control, and cybersecurity are discussed, showcasing the effectiveness of computer vision-based anomaly detection. The paper also addresses evaluation and benchmarking, including datasets and metrics. Future directions include multi-modal anomaly detection, real-time detection, and the integration of domain knowledge. This paper serves as a valuable resource for researchers and practitioners interested in utilizing computer vision for anomaly detection.

Anomaly detection, also known as outlier detection, is a fundamental task in data analysis and pattern recognition. It involves identifying instances or patterns that deviate significantly from the norm or expected behavior within a given dataset. Anomalies can arise due to various factors, such as errors in data collection, rare events, fraudulent activities, or system malfunctions. Detecting anomalies is crucial in numerous domains, including finance, healthcare, cybersecurity, manufacturing, and transportation, as anomalies often indicate critical and potentially harmful events.