Comparative analysis of video anomaly detection algorithms

Abstract

With the continuous development of deep learning, anomaly detection technology in computer vision has maderemarkable progress. Video anomaly detection is essential to ensure public safety. We classify and summarize anomalydetection based on deep learning. First, the overall process of anomaly detection is presented. Then, based on the neural network training method, we discuss the development and application of deep learning in the field of anomaly detectionfrom four aspects: Multiple Instance Learning, Regression models, Clustering models, and Reconstruction models. Finally, we present commonly used datasets and performance evaluation criteria , analyze the performance of different algorithms and discuss the future directions.

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“…In a related vein, (8) explores the growing interest in anomaly detection in video surveillance systems, driven by the demand for automated tools that can identify unusual events in video streams to enhance public safety. Shifting gears to image-to-image translation, (9) investigates the use of conditional adversarial networks as a universal solution. These networks, capable of learning both image transformation and loss functions, provide a singular approach for diverse tasks.…”