Video Anomaly Detection Using Pre-Trained Deep Convolutional Neural Nets and Context Mining

TL;DR

This paper presents a resource-efficient video anomaly detection method using pre-trained deep CNNs and context mining, suitable for IoT edge devices, achieving competitive results with low complexity.

Contribution

It introduces a novel approach combining pre-trained CNN feature extraction with context mining and autoencoders for efficient anomaly detection.

Findings

Achieves comparable performance to state-of-the-art methods on UCSD datasets.

Utilizes low-complexity models suitable for resource-constrained devices.

Demonstrates effectiveness of high-level feature and context-based analysis.

Abstract

Anomaly 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.