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.
In video surveillance, classification of visual data can be very hard, due to the scarce resolution and the noise characterizing the sensors’ data. In this paper, we propose a novel feature, the ARray of COvariances (ARCO), and a multi-class classification framework operating on Riemannian manifolds. ARCO is composed by a structure of covariance matrices of image features, able to extract information...
In surveillance applications, head and body orientation of people is of primary importance for assessing many behavioral traits. Unfortunately, in this context people are often encoded by a few, noisy pixels so that their characterization is difficult. We face this issue, proposing a computational framework which is based on an expressive descriptor, the covariance of features. Covariances have been...
In pattern recognition, two of the main paradigms for describing objects are the feature-based and the (dis)similarity-based one. The former aims at encoding tangible features that characterize the object per-se. The latter gives a relational description of the object, considering the similarities with other reference entities. In this paper, we propose the marriage between these two philosophies:...
Pedestrian detection is a crucial task in several video surveillance and automotive scenarios, but only a few detection systems are designed to be realized on an embedded architecture, allowing to increase the processing speed which is one of the key requirements in real applications. In this paper, we propose a novel SoC (System on Chip) architecture for fast pedestrian detection in video. Our implementation...
In this paper we propose a pedestrian detection algorithm and its implementation on a Xilinx Virtex-4 FPGA. The algorithm is a sliding window-based classifier, that exploits a recently designed descriptor, the covariance of features, for characterizing pedestrians in a robust way. In the paper we show how such descriptor, originally suited for maximizing accuracy performances without caring about...
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.