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

An original framework for understanding human actions and body language by using deep neural networks

The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig