Visible to the public Video Anomaly Detection using Pre-Trained Deep Convolutional Neural Nets and Context Mining

TitleVideo Anomaly Detection using Pre-Trained Deep Convolutional Neural Nets and Context Mining
Publication TypeConference Paper
Year of Publication2020
AuthorsWu, Chongke, Shao, Sicong, Tunc, Cihan, Hariri, Salim
Conference Name2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA)
Date PublishedNov. 2020
ISBN Number978-1-7281-8577-4
Keywordsabnormal event detection, anomaly detection, anomaly video analysis, Autonomic Security, Cameras, composability, Computational modeling, context mining, deep features, feature extraction, pubcrawl, resilience, Resiliency, security, Semantics, Streaming media, Task Analysis, video surveillance
AbstractAnomaly detection is critically important for intelligent surveillance systems to detect in a timely manner any malicious activities. Many video anomaly detection approaches using deep learning methods focus on a single camera video stream with a fixed scenario. These deep learning methods use large-scale training data with large complexity. As a solution, in this paper, we show how to use pre-trained convolutional neural net models to perform feature extraction and context mining, and then use denoising autoencoder with relatively low model complexity to provide efficient and accurate surveillance anomaly detection, which can be useful for the resource-constrained devices such as edge devices of the Internet of Things (IoT). Our anomaly detection model makes decisions based on the high-level features derived from the selected embedded computer vision models such as object classification and object detection. Additionally, we derive contextual properties from the high-level features to further improve the performance of our video anomaly detection method. We use two UCSD datasets to demonstrate that our approach with relatively low model complexity can achieve comparable performance compared to the state-of-the-art approaches.
Citation Keywu_video_2020