On Deep Machine Learning Methods for Anomaly Detection within Computer Vision

This thesis concerns deep learning approaches for anomaly detection in images. Anomaly detection addresses how to find any kind of pattern that differs from the regularities found in normal data and is receiving increasingly more attention in deep learning research. This is due in part to its wide set of potential applications ranging from automated CCTV surveillance to quality control across a range of industries. We introduce three original methods for anomaly detection applicable to two specific deployment scenarios. In the first, we detect anomalous activity in potentially crowded scenes through imagery captured via CCTV or other video recording devices. In the second, we segment defects in textures and demonstrate use cases representative of automated quality inspection on industrial production lines. In the context of detecting anomalous activity in scenes, we take an existing state-of-the-art method and introduce several enhancements including the use of a region proposal network for region extraction and a more information-preserving feature preprocessing strategy. This results in a simpler method that is significantly faster and suitable for real-time application. In addition, the increased efficiency facilitates building higher-dimensional models capable of improved anomaly detection performance, which we demonstrate on the pedestrian-based UCSD Ped2 dataset. In the context of texture defect detection, we introduce a method based on the idea of texture restoration that surpasses all state-of-the-art methods on the texture classes of the challenging MVTecAD dataset. In the same context, we additionally introduce a method that utilises transformer networks for future pixel and feature prediction. This novel method is able to perform competitive anomaly detection on most of the challenging MVTecAD dataset texture classes and illustrates both the promise and limitations of state-of-the-art deep learning transformers for the task of texture anomaly detection

Learning Deep Representations of Appearance and Motion for Anomalous Event Detection

We present a novel unsupervised deep learning framework for anomalous event detection in complex video scenes. While most existing works merely use hand-crafted appearance and motion features, we propose Appearance and Motion DeepNet (AMDN) which utilizes deep neural networks to automatically learn feature representations. To exploit the complementary information of both appearance and motion patterns, we introduce a novel double fusion framework, combining both the benefits of traditional early fusion and late fusion strategies. Specifically, stacked denoising autoencoders are proposed to separately learn both appearance and motion features as well as a joint representation (early fusion). Based on the learned representations, multiple one-class SVM models are used to predict the anomaly scores of each input, which are then integrated with a late fusion strategy for final anomaly detection. We evaluate the proposed method on two publicly available video surveillance datasets, showing competitive performance with respect to state of the art approaches.Comment: Oral paper in BMVC 201

Abnormal Event Detection in Videos using Spatiotemporal Autoencoder

We present an efficient method for detecting anomalies in videos. Recent applications of convolutional neural networks have shown promises of convolutional layers for object detection and recognition, especially in images. However, convolutional neural networks are supervised and require labels as learning signals. We propose a spatiotemporal architecture for anomaly detection in videos including crowded scenes. Our architecture includes two main components, one for spatial feature representation, and one for learning the temporal evolution of the spatial features. Experimental results on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of our method is comparable to state-of-the-art methods at a considerable speed of up to 140 fps

Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks

Intrusion detection has become one of the most critical tasks in a wireless network to prevent service outages that can take long to fix. The sheer variety of anomalous events necessitates adopting cognitive anomaly detection methods instead of the traditional signature-based detection techniques. This paper proposes an anomaly detection methodology for wireless systems that is based on monitoring and analyzing radio frequency (RF) spectrum activities. Our detection technique leverages an existing solution for the video prediction problem, and uses it on image sequences generated from monitoring the wireless spectrum. The deep predictive coding network is trained with images corresponding to the normal behavior of the system, and whenever there is an anomaly, its detection is triggered by the deviation between the actual and predicted behavior. For our analysis, we use the images generated from the time-frequency spectrograms and spectral correlation functions of the received RF signal. We test our technique on a dataset which contains anomalies such as jamming, chirping of transmitters, spectrum hijacking, and node failure, and evaluate its performance using standard classifier metrics: detection ratio, and false alarm rate. Simulation results demonstrate that the proposed methodology effectively detects many unforeseen anomalous events in real time. We discuss the applications, which encompass industrial IoT, autonomous vehicle control and mission-critical communications services.Comment: 7 pages, 7 figures, Communications Workshop ICC'1

Activity Recognition based on a Magnitude-Orientation Stream Network

The temporal component of videos provides an important clue for activity recognition, as a number of activities can be reliably recognized based on the motion information. In view of that, this work proposes a novel temporal stream for two-stream convolutional networks based on images computed from the optical flow magnitude and orientation, named Magnitude-Orientation Stream (MOS), to learn the motion in a better and richer manner. Our method applies simple nonlinear transformations on the vertical and horizontal components of the optical flow to generate input images for the temporal stream. Experimental results, carried on two well-known datasets (HMDB51 and UCF101), demonstrate that using our proposed temporal stream as input to existing neural network architectures can improve their performance for activity recognition. Results demonstrate that our temporal stream provides complementary information able to improve the classical two-stream methods, indicating the suitability of our approach to be used as a temporal video representation.Comment: 8 pages, SIBGRAPI 201