A comprehensive review on 3D object detection and 6D pose estimation with deep learning

Nowadays, computer vision with 3D (dimension) object detection and 6D (degree of freedom) pose assumptions are widely discussed and studied in the field. In the 3D object detection process, classifications are centered on the object's size, position, and direction. And in 6D pose assumptions, networks emphasize 3D translation and rotation vectors. Successful application of these strategies can have a huge impact on various machine learning-based applications, including the autonomous vehicles, the robotics industry, and the augmented reality sector. Although extensive work has been done on 3D object detection with a pose assumption from RGB images, the challenges have not been fully resolved. Our analysis provides a comprehensive review of the proposed contemporary techniques for complete 3D object detection and the recovery of 6D pose assumptions of an object. In this review research paper, we have discussed several proposed sophisticated methods in 3D object detection and 6D pose estimation, including some popular data sets, evaluation matrix, and proposed method challenges. Most importantly, this study makes an effort to offer some possible future directions in 3D object detection and 6D pose estimation. We accept the autonomous vehicle as the sample case for this detailed review. Finally, this review provides a complete overview of the latest in-depth learning-based research studies related to 3D object detection and 6D pose estimation systems and points out a comparison between some popular frameworks. To be more concise, we propose a detailed summary of the state-of-the-art techniques of modern deep learning-based object detection and pose estimation models

Advances and Applications of Computer Vision Techniques in Vehicle Trajectory Generation and Surrogate Traffic Safety Indicators

The application of Computer Vision (CV) techniques massively stimulates microscopic traffic safety analysis from the perspective of traffic conflicts and near misses, which is usually measured using Surrogate Safety Measures (SSM). However, as video processing and traffic safety modeling are two separate research domains and few research have focused on systematically bridging the gap between them, it is necessary to provide transportation researchers and practitioners with corresponding guidance. With this aim in mind, this paper focuses on reviewing the applications of CV techniques in traffic safety modeling using SSM and suggesting the best way forward. The CV algorithm that are used for vehicle detection and tracking from early approaches to the state-of-the-art models are summarized at a high level. Then, the video pre-processing and post-processing techniques for vehicle trajectory extraction are introduced. A detailed review of SSMs for vehicle trajectory data along with their application on traffic safety analysis is presented. Finally, practical issues in traffic video processing and SSM-based safety analysis are discussed, and the available or potential solutions are provided. This review is expected to assist transportation researchers and engineers with the selection of suitable CV techniques for video processing, and the usage of SSMs for various traffic safety research objectives

Deep Learning-Based Action Recognition

The classification of human action or behavior patterns is very important for analyzing situations in the field and maintaining social safety. This book focuses on recent research findings on recognizing human action patterns. Technology for the recognition of human action pattern includes the processing technology of human behavior data for learning, technology of expressing feature values ​​of images, technology of extracting spatiotemporal information of images, technology of recognizing human posture, and technology of gesture recognition. Research on these technologies has recently been conducted using general deep learning network modeling of artificial intelligence technology, and excellent research results have been included in this edition

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Audio for Virtual, Augmented and Mixed Realities: Proceedings of ICSA 2019 ; 5th International Conference on Spatial Audio ; September 26th to 28th, 2019, Ilmenau, Germany

The ICSA 2019 focuses on a multidisciplinary bringing together of developers, scientists, users, and content creators of and for spatial audio systems and services. A special focus is on audio for so-called virtual, augmented, and mixed realities. The fields of ICSA 2019 are: - Development and scientific investigation of technical systems and services for spatial audio recording, processing and reproduction / - Creation of content for reproduction via spatial audio systems and services / - Use and application of spatial audio systems and content presentation services / - Media impact of content and spatial audio systems and services from the point of view of media science. The ICSA 2019 is organized by VDT and TU Ilmenau with support of Fraunhofer Institute for Digital Media Technology IDMT