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 one of the key technologies in automotive safety, robotic and intelligent video surveillance. Recently, deep convolutional neural networks have achieved significant effect in image classification and retrieval tasks. In this paper, we propose a novel deep convolutional neural networks model for pedestrian detection to simultaneously extract and classify pedestrian features...
In this paper we propose a novel multiple target tracking model composed of two detectors and a tracker. An on-line detector and a tracker are used to generate target candidates, whose confidence scores are then evaluated by the off-line trained detectors. In the data association stage, the high-efficient inference in a structural model leads to the optimal tracking result. The experimental results...
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.