Entropy in Image Analysis III

Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent research field is not only to mimic the human vision system. Image analysis is the main methods that computers are using today, and there is body of knowledge that they will be able to manage in a totally unsupervised manner in future, thanks to their artificial intelligence. The articles published in the book clearly show such a future

Human Action Recognition for Intelligent Video Surveillance

DissertationCrime remains a persistent threat in South Africa. This has significant implications for our ability to function as a country. As a result, there is a dire need for crime prevention strategies and measures that seek to reduce the risk of crimes occurring, and their potential harmful effects on individuals and society. Many local businesses, organisations and homes utilise video surveillance as a measure, as it can capture the crime as it is committed, thus identifying the perpetrators, or at least presenting a few suspects. In current video surveillance systems, there is no software that enables security officers to manage the data collected (i.e. automatically describe activities occurring in the video) and make it easily accessible for query and investigation. Access to the data is difficult because of the nature and size of the data. There is a need for efficiently extracting data to automatically detect, track, and recognise objects of interest, including understanding and analysing data through intelligent video surveillance. The aim of the study is to create an intelligent vision system that can identify a range of human actions in surveillance videos. This would offer security officers additional data of activities occurring in the videos, thus enabling them to access specific incidents faster and provide early detections of crimes. To achieve this, a literature study was done in the research area to reveal the prerequisites for such systems, the separate software modules designed and developed and eventually integrated into the intended system. Tests were developed to validate the system and evaluate how all the modules work together. This inevitably confirms the functionality of the fundamental components and the system in its entirety. The results have indicated that each module in the system operates successfully, can effectively extract pose estimation features, generate features for training/ classification and classify the features using a deep neural network. Further results showed that capability of the system can be applied to intelligent surveillance systems and enable security officers’ early detection of abnormal behaviour that can lead to crime

Modeling Deep Context in Spatial and Temporal Domain

Context has been one of the most important aspects in computer vision researches because it provides useful guidance to solve variant tasks in both spatial and temporal domain. As the recent rise of deep learning methods, deep networks have shown impressive performances on many computer vision tasks. Model deep context explicitly and implicitly in deep networks can further boost the effectiveness and efficiency of deep models. In spatial domain, implicitly model context can be useful to learn discriminative texture representations. We present an effective deep fusion architecture to capture both the second order and first older statistics of texture features; Meanwhile, explicitly model context can also be important to challenging task such as fine-grained classification. We then present a deep multi-task network that explicitly captures geometry constraints by simultaneously conducting fine-grained classification and key-point localization. In temporal domain, explicitly model context can be crucial to activity recognition and localization. We present a temporal context network to explicitly capture relative context around a proposal, which samples two temporal scales pair-wisely for precise temporal localization of human activities; Meanwhile, implicitly model context can lead to better network architecture for video applications. We then present a temporal aggregation network that learns a deep hierarchical representation for capturing temporal consistency. Finally, we conduct research on jointly modeling context in both spatial and temporal domain for human action understanding, which requires to predict where, when and what a human action happens in a crowd scene. We present a decoupled framework that has dedicated branches for spatial localization and temporal recognition. Contexts in spatial and temporal branches are modeled explicitly and fused together later to generate final predictions

Digital Interaction and Machine Intelligence

This book is open access, which means that you have free and unlimited access. This book presents the Proceedings of the 9th Machine Intelligence and Digital Interaction Conference. Significant progress in the development of artificial intelligence (AI) and its wider use in many interactive products are quickly transforming further areas of our life, which results in the emergence of various new social phenomena. Many countries have been making efforts to understand these phenomena and find answers on how to put the development of artificial intelligence on the right track to support the common good of people and societies. These attempts require interdisciplinary actions, covering not only science disciplines involved in the development of artificial intelligence and human-computer interaction but also close cooperation between researchers and practitioners. For this reason, the main goal of the MIDI conference held on 9-10.12.2021 as a virtual event is to integrate two, until recently, independent fields of research in computer science: broadly understood artificial intelligence and human-technology interaction

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others

A new perspective on urban form with the integration of Space Syntax and MCDA – An exploratory analysis of the city of Xi’an, China

The research on urban form and its related socio-economic activities have been undertaken for long, while the evolution of research approaches and tools are limited by the conventional medium such as maps to interpret the built environment. Emerging new urban data has equipped urban morphologists with innovative datasets for urban form studies. This thesis aims to explore and interpret the urban form from a new perspective, which integrates spatial patterns generated from Space Syntax analysis with new urban data in GIS using Multi-Criteria Decision Analysis (MCDA). Xi’an, as one of the most renowned historic cities in China, is selected as the case since the city has undergone an unprecedented transformation of urban form in the last decades. In this research, the space syntax model of Xi’an is built primarily based on the OpenStreetMap (OSM) with further refinement by the Baidu Map. The Point of Interests (POI) data acquired from the Baidu Map API serves as the main datasets for the criterion of MCDA. The social data of human movement flow is gathered by the gate observation in five representative districts in Xi’an. The correlation analysis with observation data is employed to compare the result of the original Space Syntax analysis and MCDA to consolidate the understanding of urban form. This research provides urban researchers with new insights into urban form in terms of the application of new urban data, the invention of a new research method, and the new perception of the City of Xi’an. The findings suggest that the new perspective can facilitate the quantifying process and enhance the comprehensive understanding of urban form. Based on the research, recommendations for further studies and urban planning of Xi’an are discussed