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Travel independently is one of the core skills required for children with intellectual disability to lead an independent life. Virtual reality based travel training system with natural interaction could help them to practice their travel skill in a safe environment without supervision. Developing real-time travel training system is a challenging work due to inherent complexity of 3D traffic simulation...
Vehicle detection can provide volumes of useful data for city planning and transport management. It has always been a challenging task because of various complicated backgrounds and the relatively small sizes of targets, especially in high resolution satellite images. A novel model called joint-layer deep convolutional neural networks (JLDCNNs), which joins features in the higher layers and the lower...
Kangaroo vehicle collisions are a serious problem threatening the safety of the drivers on Australian roads. It is estimated, according to a recent report by Australian Associated Motor Insurers, that there are around 20,000 kangaroo vehicle collisions during year 2015 in Australia. As a result, more than AU \$75 million in insurance claims, and a number of animal and human severe injuries and fatalities...
Unfamiliar urban intersections pose high demand on drivers. They are not only engaged in correctly assessing large amount of visual stimuli, including multiple diverse moving objects (e.g. other vehicles, pedestrians, cyclists) but also actively processing instructions provided by navigation system, either in-car or on other devices such as smart-phones. In such a highly dynamic and engaging situation,...
Vehicle detection is an essential task in an intelligent vehicle. Despite being a well-studied vision problem, it is unclear how well vehicle detectors generalize to new settings. Specifically, this paper studies the generalization capability of vehicle detectors on a U.S. highway dataset. Two types of models are employed in the experimental analysis, a subcategory aggregate channel features model...
In metropolitan areas, about 50% of traffic delays are caused by non-recurring traffic incidents. Hence, accurate prediction of the duration of such events is critical for traffic management authorities. In this paper, we study the predictability of the duration of traffic incidents by considering various external factors. As incident data is typically sparse, training a large number of models (for...
The driver's face is key to a less intrusive method to monitoring the driver to derive information such as distraction, drowsiness, intent, and where they are looking. A vital step in extracting these higher level information is to find the driver's face and individual components such as eyes, nose and mouth, along with the direction they are facing towards. In the context of safety critical situation...
Nowadays, agricultural and mining industry applications require saving energy in mobile robotic tasks. This critical issue encouraged us to enhance the performance of path tracking controllers during manoeuvring over slippery and rough terrains. In this scenario, we propose probabilistic approaches under machine learning schemes in order to optimally self-tune the controller. The approaches are real...
A driver's inattention to or disregard for the minimum safety distance creates a hazardous situation in which avoiding a rear-end collision is nearly impossible. In a joint effort to implement safety and decision-making processes at an individual level, we present in this paper a cooperative approach to increase the driver's visual awareness of safe distances. Our Tailigator system garners information...
Automatic traffic sign detection and recognition is a field of computer vision which is very important aspect for advanced driver support system. This paper proposes a framework that will detect and classify different types of traffic signs from images. The technique consists of two main modules: road sign detection, and classification and recognition. In the first step, colour space conversion, colour...
In the past decade, improvements in the production of in-expensive PC equipment and software has permitted more refined real-time signal processing in BCI systems. In the literature, Deep learning concepts have not been applied to EEG data analysis in a systematic manner. This paper applies various existing Deep learning architectures and algorithms for the classification of EEG data applied to eye...
Advanced driver assistance systems are required to detect latent hazards posed by surrounding vehicles and generate an appropriate response to enhance safety. Lane changes constitute potentially risky maneuvers, as drivers involved encounter latent hazards due to surrounding vehicles. A careful study of lane change behavior is therefore essential in identifying potential abnormalities that may lead...
Emerging self-driving vehicles are vulnerable to different attacks due to the principle and the type of communication systems that are used in these vehicles. These vehicles are increasingly relying on external communication via vehicular ad hoc networks (VANETs). VANETs add new threats to self-driving vehicles that contribute to substantial challenges in autonomous systems. These communication systems...
This document describes a distance measurement system for underwater applications with computer vision and an Artificial Neural Network (ANN). The developed system is installed in the observation class remotely operated vehicle (ROV) Visor3, to be used in experimental testing. An integrated camera with two laser pointers are used to obtain input data to a Multi-Layer Perceptron (MLP) ANN, trained...
Estimating the number of vehicles present in traffic video sequences is a common task in applications such as active traffic management and automated route planning. There exist several vehicle counting methods such as Particle Filtering or Headlight Detection, among others. Although Principal Component Pursuit (PCP) is considered to be the state-of-the-art for video background modeling, it has not...
Over the past years, automatic traffic accident detection (ATAD) based on video has become one of the most promising applications in intelligent transportation and is playing a more and more important role in ensuring travel safety. This paper proposes a classifier-based supervised method by viewing the last seconds before motor vehicle collisions as the detection target. In our method, we devise...
In this paper, we propose the dense disparity map-based pedestrian detection method for intelligent vehicle. The dense disparity map is utilized to improve the pedestrian detection performance. Our method consists of several steps namely, obstacle area detection using road feature information and column detection, pedestrian area detection using dense disparity map-based segmentation, and pedestrian...
Classification of electroencephalography (EEG)-based application is one of the important process for biomedical engineering. Driver fatigue is a major case of traffic accidents worldwide and considered as a significant problem in recent decades. In this paper, a hybrid deep generic model (DGM)-based support vector machine is proposed for accurate detection of driver fatigue. Traditionally, a probabilistic...
In this paper, a real-time vehicle detection system is designed and implemented on an FPGA (Field Programmable Gate Array). The system is composed of an infrared camera and an image acquisition and processing board developed by our research team. An FPGA chip and a DSP chip are embedded in the image board as the major calculation units, which make realtime computation possible. First, edge features...
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