The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper introduces a novel framework for estimating the motion of a robotic car from image information, a scenario widely known as visual odometry. Most current monocular visual odometry algorithms rely on a calibrated camera model and recover relative rotation and translation by tracking image features and applying geometrical constraints. This approach has some drawbacks: translation is recovered...
This article proposes an approach to learning steering and road following behaviour from a human driver using holistic visual features. We use a random forest (RF) to regress a mapping between these features and the driver's actions, and propose an alternative to classical random forest regression based on the Medoid (RF-Medoid), that reduces the underestimation of extreme control values. We compare...
Tracking-by-detection is an attractive paradigm for intelligent visual surveillance applications where clutter, lighting variations, target overlap and occlusions hamper conventional background modeling. However, state-of-the-art vehicle and pedestrian detectors based on discriminative classification are too computationally expensive for real-time implementation on embedded smart cameras. This paper...
New vehicular safety standards require the development of pedestrian collision detection systems that can trigger the deployment of active impact alleviation measures from the vehicle prior to a collision. In this paper, we propose a new vision-based system for near-range pedestrian collision detection. The low-level system uses a bio-inspired visual neural network, which emulates the visual system...
This paper addresses the problem of using visual information to estimate vehicle motion (a.k.a. visual odometry) from a machine learning perspective. The vast majority of current visual odometry algorithms are heavily based on geometry, using a calibrated camera model to recover relative translation (up to scale) and rotation by tracking image features over time. Our method eliminates the need for...
An challenge issue in Brain-computer Interface (BCI) research is that one can hardly control his/her own EEG signals without any feedback. A suitable training protocol is necessary for an online BCI system, which provides some visual feedback allowing subjects to see their progress. Conventional feedbacks such as cursor control which subjects could easily find boring and not intuitive. In this study,...
In this paper, we introduce a fully autonomous vehicle classification system that continuously learns from largeamounts of unlabeled data. For that purpose, we proposea novel on-line co-training method based on visual and acoustic information. Our system does not need complicated microphone arrays or video calibration and automatically adapts to specific traffic scenes. These specialized detectors...
With the development of educational technology, more and more modern science and technology begins to be applied in teaching and training. Based on introduction of the definition and characters of virtual reality (VR) technology, the VR technology was introduced into the field of teaching and training. The application circumstance of VR in teaching and training was also discussed. Using VR technology,...
Auto parking techniques are attracting more attention these days. In this paper, we develop an image-based method to estimate the depth contour in parking areas. Our algorithm is an extension of the canonical appearance-based models for object recognition. One challenge in object recognition is that limited training dataset can hardly represent all kinds intra-class and inter-class variations. We...
This paper presents a novel approach for retrieving images from databases using eigen color and the concept of multiple instance learning. Usually, vehicles have various colors and shapes under different viewpoints, weathers, and lighting conditions. All the variations will increase many difficulties and challenges in selecting a general feature to describe vehicles. Thus, traditional methods to retrieve...
This paper presents the results of an experimental study on designing for self-efficacy in a game based driving simulator. Self-efficacy refers to how peoplepsilas beliefs in their capabilities affect their actions. The results show that the design of the feedback system can be used to increase self-efficacy measures thus affecting performance in a driving simulator environment. Self-efficacy has...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.