A kernel canonical correlation analysis approach for removing environmental and operational variations for structural damage identification

When talking about automation, “autonomous vehicles”, often abbreviated as AVs, come to mind. In transitioning from the “driver” mode to the different automation levels, there is an inevitable need for modeling driving behavior. This often happens through data collection from experiments and studies, but also information extraction, a key step in behavioral modeling. Particularly, naturalistic driving studies and field operational trials are used to collect meaningful data on drivers’ interactions in real–world conditions. On the other hand, information extraction methods allow to predict or mimic driving behavior, by using a set of statistical learning methods. In simple words, the way to understand drivers’ needs and wants in the era of automation can be represented in a data–information cycle, starting from data collection, and ending with information extraction. To develop this cycle, this research reviews studies with keywords “data collection”, “information extraction”, “AVs”, while keeping the focus on driving behavior. The resulting review led to a screening of about 161 papers, out of which about 30 were selected for a detailed analysis. The analysis included an investigation of the methods and equipment used for data collection, the features collected, the size and frequency of the data along with the main problems associated with the different sensory equipment; the studies also looked at the models used to extract information, including various statistical techniques used in AV studies. This paved the way to the development of a framework for data analytics and fusion, allowing the use of highly heterogeneous data to reach the defined objectives; for this paper, the example of impacts of AVs on a network level and AV acceptance is given. The authors suggest that such a framework could be extended and transferred across the various transportation sectors. 

A data–information–knowledge cycle for modeling driving behavior

As technology continues to develop, computer vision (CV) applications are becoming increasingly widespread in the intelligent transportation systems (ITS) context. These applications are developed to improve the efficiency of transportation systems, increase their level of intelligence, and enhance traffic safety. Advances in CV play an important role in solving problems in the fields of traffic monitoring and control, incident detection and management, road usage pricing, and road condition monitoring, among many others, by providing more effective methods. This survey examines CV applications in the literature, the machine learning and deep learning methods used in ITS applications, the applicability of computer vision applications in ITS contexts, the advantages these technologies offer and the difficulties they present, and future research areas and trends, with the goal of increasing the effectiveness, efficiency, and safety level of ITS. The present review, which brings together research from various sources, aims to show how computer vision techniques can help transportation systems to become smarter by presenting a holistic picture of the literature on different CV applications in the ITS context.

https://www.mdpi.com/1424-8220/23/6/2938/pdf?version=1678275415

Computer Vision Applications in Intelligent Transportation Systems: A Survey

Multiple obstacle detection is a challenging problem and has many important applications including video tracking, intelligence surveillance, robot navigation and autonomous driving. In existing methods, individual obstacle’s detection and contextual visual patterns are modeled separately and interactions from obstacles and their surroundings are mostly considered in a symmetric way, which we argue is not an optimal strategy. To tackle these difficulties, in this paper, we propose a deep convolutional networks for solving the online multiple obstacles detection problem. The method consists of deep visual information extraction and visual pattern learning. They are modeled as deep Convolution Neural Networks, which are able to learn discriminative visual features for obstacle detection and model inter-object relations in an asymmetric way and give the orientation extraction for the moving obstacles. The deep learning framework is trained in an end-to-end manner for better adapting the influences of visual information as well as inter-object relations and orientation information. Extensive experimental comparisons with state-of-the-arts as well as detailed component analysis of the proposed method on the benchmarks demonstrate the effectiveness of our proposed framework.

Multiple Obstacle Detection for Assistance Driver System Using Deep Neural Networks

Multiset integrated canonical correlation analysis (MICCA) can distinctly express the integral correlation among multi-group feature. Thus, MICCA is very powerful for multiple feature extraction. However, it is difficult to capture nonlinear relationships with the linear mapping. In order to overcome this problem, we, in this paper, propose a multi-kernel multiset integrated canonical correlation analysis (MK-MICCA) framework for subspace learning. In the MK-MICCA framework, the input data of each feature are mapped into multiple higher dimensional feature spaces by implicitly nonlinear mappings determined by different kernels. This enables MK-MICCA to uncover a variety of different geometrical structures of the original data in the feature spaces. Extensive experimental results on multiple feature database and ORL database show that MK-MICCA is very effective and obviously outperforms the single-kernel-based MICCA.

A Novel Nonlinear Multi-feature Fusion Algorithm: Multiple Kernel Multiset Integrated Canonical Correlation Analysis.

The invention relates to an intelligent sensing system and method for assisting visual impaired patients in going out safely. The system comprises a visual image sensor, a radar ranging sensor, a prompt device and a processing device, wherein the processing device is electrically connected with the visual image sensor, the radar ranging sensor and the prompt device respectively. The intelligent sensing system and method for assisting the visual impaired patients in going out safely can effectively ensure the safety of the visual impaired patients when going out.

Intelligent sensing system and method for assisting visual impaired patients in going out safely

Semantic annotation has attracted a growing interest in the information retrieval and computer vision. Existing methods have typically focused on several visual cues and semantic context information with an image itself using different frameworks, neglecting the prior knowledge constraints about the real world. However, strong prior knowledge embedding should be considered to improve the performance of semantic annotation tasks. Note that semantic objects will interact each other during the semantic prediction stage, and the support visual relationships can affect the recall and accuracy of semantic annotations. In this paper, we exploit a novel method to semantic modeling with prior knowledge embedding to jointly find the semantic objects and the corresponding support relationships in the images. Inference in the model can be conducted exactly via graph modeling and knowledge embedding, and the parameters can be learned at the supervised learning stage. The extensive experiments on COCO15 and Stanford Visual Relationship data sets confirm the benefits of semantic annotation for the objects for the knowledge embedding.

Jing Yang

Papers

Multiple Obstacle Detection for Assistance Driver System Using Deep Neural Networks

A Novel Nonlinear Multi-feature Fusion Algorithm: Multiple Kernel Multiset Integrated Canonical Correlation Analysis.

Intelligent sensing system and method for assisting visual impaired patients in going out safely

Improving Semantic Annotation Using Semantic Modeling of Knowledge Embedding