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.
Pedestrian detection is a canonical problem in computer vision. Motivated by the observation that the major bottleneck of pedestrian detection lies on the different scales of pedestrian instances in images, our effort is focused on improving the detection rate, especially for small-sized pedestrians who are relatively far from the camera. In this paper, we introduce a novel context-aware pedestrian...
The vision-based lane detection is an important component of advanced driver assistance systems and it is essential for lane departure warning, lane keeping, and vehicle localisation. However, it is a challenging problem to improve the robustness of multi-lane detection due to factors, such as perspective effect, possible low visibility of lanes, and partial occlusions. To deal with these issues,...
This paper introduces a discriminative framework for the task of vehicle detection based on Hough Forest. The leaf nodes in Hough Forest framework are not discriminative enough, which means that they do not have the ability to classify whether the test patches ended up in each leaf are positive or negative. Hough votes are assigned to all test patches by Hough forest, including negative test patches,...
This paper presents a novel pedestrian detection framework for efficient detection of both unoccluded and occluded pedestrians, thereby proposing an efficient technique for pedestrian detection in real-time environment. Our framework consists of two layers of detection, the first layer using full body detectors for accurate detection of unoccluded pedestrians and then a cascaded layer of part based...
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.