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This work presents initial findings of a research project aimed at designing an assistance system able to improve driver ability and reduce accident risk. The proposed system is an innovative Advanced Driver Assistance System based on the integration of two main components: a training procedure based on precision teaching, and a control equipment monitoring driver behavior and providing feedbacks...
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...
Vehicle Logo Recognition(VLR) has been an important study field in intelligent Transportation system (ITS). This paper proposes to recognize vehicle logo and predict logo attributes by combining Convolutional Neural Network (CNN) with Multi-Task Learning(MTL). In order to accelerate convergence of multi-task model, an adaptive weight training strategy is employed. To verify the algorithm, the Xiamen...
We take a look at current state of traffic sign classification discussing what makes it a specific problem of visual object classification. With impressive state-of-the-art results it is easy to forget that the domain extends beyond annotated datasets and overlook the problems that must be faced before we can start training classifiers. We discuss such problems, give an overview of previous work done,...
Traditional vehicle recognition and retrieval systems are almost based on vehicle license plate recognition, which requires the user to provide images containing the license plate of the vehicle. In order to recognize and retrieve vehicles more convenient and efficient, and solve the problem of missing or wrong license plate, this paper proposes a method based on the overall appearance characteristics...
In this paper, we will report our virtual evaluation tool which can be used for a simultaneous evaluation of driving acuity and spatial perceptual capacity. This system has been developed in a multidisciplinary team consisting of systemic neuroscientists and computer scientists. Our approach is to develop a portable system which is cost effective to be used in hospitals, driving schools or even police...
In this paper we present a method for calculating inertial motion feedback in a teleoperation setup. For this we make a distinction between vehicle-state feedback that depends on the physical motion of the remote vehicle, and task-related motion feedback that provides information about the teleoperation task. By providing motion feedback that is independent of vehicle motion we exploit the spatial...
In the paper, comparative studies of three projection systems was carried out, i.e., With a cylindrical screen, with rear projection on foil placed on car windows -- "on screen", and with a collimation system. The purpose of the study was to assess the performance characteristics of visualization systems and the susceptibility of trainees to symptoms of simulator sickness. The results indicated...
Obstacle detection for advanced driver assistance systems has focused on building detectors for only a few number of object categories so far, such as pedestrians and cars. However, vulnerable obstacles of other categories are often dismissed, such as wheel-chairs and baby strollers. In our work, we try to tackle this limitation by presenting an approach which is able to predict the vulnerability...
Logo identification and classification have received considerable attention from both the machine learning and computer vision communities. Vehicle logo recognition (VLR) is used to recognise accurately the manufacturer of a vehicle by using its iconic logo. A VLR system in addition to license plate recognition aims to increase the confidence of vehicle monitoring systems in private environments such...
In densely populated areas, we currently see a paradigm shift in personal mobility. For the younger generation, car usership is gradually replacing the need of car ownership. However, for example, when relying on car sharing solutions, users often spontaneously drive cars they are not used to. Results are increased stress and a higher risk of accidents. For that reason, we present a mobile application-based...
In this paper, we present a novel interface for teleoperating ground vehicles. Obstacle avoidance with ground vehicles demands a high level of operator attention, typically distracting from the primary mission. The Ambient Obstacle Avoidance (AOA) was designed to allow operators to effectively perform a primary task, such as search, while still effectively avoiding obstacles. The AOA wraps around...
In this paper, we present a novel framework for representation of images as a combination of multiple mid-level feature descriptor representation based group of visual words. The mid-level feature representation is computed on discriminative patches of the image to build a lexicon, the visual words of which are used to represent the shape within that image. The proposed image representation method...
We here study the problem of visual attention computation in video of driving environment via the learning from eye movements. We collect a large-scale database of eye movements from 28 subjects on 30 videos of road scenes, which simulate the driving environment. The analysis on this eye movement database reveals that visual attention in driving environment is directed by high-level cognitive factors...
This paper presents a new method for the vehicle license plate and the frontal mask localization. The proposed license plate localization initializes candidate regions based on maximally stable extremal regions (MSERs). Then, the candidate regions are categorized into three classes of license plate character components, plate background components and the other components by using intensity, size,...
Vehicle recognition is a challenging task with many useful applications. State-of-the-art methods usually learn discriminative classifiers for different vehicle categories or different viewpoint angles, but little work has explored vehicle recognition using semantic visual attributes. In this paper, we propose a novel iterative multiple instance learning method to model local attributes and viewpoint...
This paper presents an overview of literature on human factors in driving, with a view to introduce systems perspective in driver training and evaluation, as well as the need to review road transport policies in India. There is a steady increase in traffic fatalities in spite of the presence of traffic rules, regulations and legislative systems all over the country. One possible reason for this is...
This paper addresses the problem of discovering activities and their temporal significance in surveillance videos in an unsupervised manner. We propose a generative model that can jointly capture the activities and their behaviour over time. We use multinomial distribution over local motion features to model activities and a mixture distribution over their time stamps to capture the multi-modal temporal...
ImageNet is a large-scale database of object classes with millions of images. Unfortunately only a small fraction of them is manually annotated with bounding-boxes. This prevents useful developments, such as learning reliable object detectors for thousands of classes. In this paper we propose to automatically populate ImageNet with many more bounding-boxes, by leveraging existing manual annotations...
The accident probability of beginner drivers is significantly higher than that of experienced drivers. It can be assumed that this is due to lack of driving skills which lead to making wrong decisions according to cognition and operating in correct way. In this paper, we propose a novel assisted driving system intended to help drivers to improve their skills for the reverse parking. The system is...
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