Current Issue : October - December Volume : 2011 Issue Number : 1 Articles : 4 Articles
Recently, because of its importance in computer vision and surveillance systems, object tracking has progressed rapidly over the last two decades. Researches on such systems still face several theoretical and technical problems that badly impact not only the accuracy of position measurements but also the continuity of tracking. In this paper, a novel strategy for tracking multiple objects using static cameras is introduced, which can be used to grant a cheap, easy installation and robust tracking system. The proposed tracking strategy is based on scenes captured by a number of static video cameras. Each camera is attached to a workstation that analyzes its stream. All workstations are connected directly to the tracking server, which harmonizes the system, collects the data, and creates the output spatial-tempo database. Our contribution comes in two issues. The first is to present a new methodology for transforming the image coordinates of an object to its real coordinates. The second is to offer a flexible event-based object tracking strategy. The proposed tracking strategy has been tested over a CAD of soccer game environment. Preliminary experimental results show the robust performance of the proposed tracking strategy....
We investigate the video assignment problem of a hierarchical Video-on-Demand (VOD) system in heterogeneous environments where different quality levels of videos can be encoded using either replication or layering. In such systems, videos are delivered to clients either through a proxy server or video broadcast/unicast channels. The objective of our work is to determine the appropriate coding strategy as well as the suitable delivery mechanism for a specific quality level of a video such that the overall system blocking probability is minimized. In order to find a near-optimal solution for such a complex video assignment problem, an evolutionary approach based on genetic algorithm (GA) is proposed. From the results, it is shown that the system performance can be significantly enhanced by efficiently coupling the various techniques....
The number of items that users can now access when navigating on the Web is so huge that these might feel lost. Recommender systems are a way to cope with this profusion of data by suggesting items that fit the users needs. One of the most popular techniques for recommender systems is the collaborative filtering approach that relies on the preferences of items expressed by users, usually under the form of ratings. In the absence of ratings, classical collaborative filtering techniques cannot be applied. Fortunately, the behavior of users, such as their consultations, can be collected. In this paper, we present a new approach to perform collaborative filtering when no rating is available but when user consultations are known. We propose to take inspiration from local community detection algorithms to form communities of users and deduce the set of mentors of a given user. We adapt one state-of-the-art algorithm so as to fit the characteristics of collaborative filtering. Experiments conducted show that the precision achieved is higher then the baseline that does not perform any mentor selection. In addition, our model almost offsets the absence of ratings by exploiting a reduced set of mentors....
An approach of representing meanings of images based on associative values with lexicons is proposed. For this, the semantic tolerance relation model (STRM) that reflects the tolerance degree between defined lexicons is generated, and two factors of semantic relevance (SR) and visual similarity (VS) are involved in generating associative values. Furthermore, the algorithm of calculating associative values using pixel-based bidirectional associative memories (BAMs) in combination with the STRM, which is easy in implementation, is depicted. The experiment results of multilexicons-based retrieval by individuals show the effectiveness and efficiency of our proposed method in finding the expected images and the improvement in retrieving accuracy because of incorporating SR with VS in representing meanings of images....
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