We present a novel inter-camera trajectory association algorithm for partially overlapping visual sensor networks. The approach consists of three steps, namely Extraction, Representation and Association. Firstly, we extract trajectory segments in each camera view independently. These local trajectory segments are then projected on a common-plane. Next, we learn dynamic motion models of the projected trajectory segments using Modified Consistent Akaike’s Information Criterion (MCAIC). These models help in removing noisy observations from a segment and hence perform smoothing efficiently. Then, each smoothed trajectory is represented by its curvature. Finally, we use normalized cross correlation, as a proximity measure, to establish correspondence among trajectories that are observed in multiple views. We evaluated the performance of the proposed approach on a simulated and real scenarios with simultaneous moving objects observed by multiple cameras and compared it with state-of-the-art algorithms. Convincing results are observed in favor of the proposed approach.

This paper presents AES (acronym of Agents for EnrichedServices) framework; a generic framework that can be used to enrich services and adapting an original service request to what the context information, user profile and other multiple sources of information can bring in order to improve the end-user experience in using the framework. The framework is presented along with its architecture and the way it was implemented using agents, an example showing how this framework may improve collaboration and a study case are presented in order to validate the framework's functionality.

One of the current challenges in information technology is to provide instant access to relevant information, without being limited by the location of the user or the device used to retrieve it. Current technologies allow users to access an overwhelming amount of data. This is an undesirable situation, since it is very difficult for users to obtain relevant information from these data. Enriching the user experience, which means, adding criteria such as user preferences and characteristics of his/her mobile context of use (e.g., location, access device features), can solve the aforementioned problem, since the information can be better adjusted to user needs and space-time situation, reducing the volume of information received by the user and avoiding information overloading. This paper presents an approach to enrich the user experience called Agents for Enrichment Services (AES), an adaptation framework based on agents that provides users with information tailored to their specific needs, features, devices, and context, with the purpose of giving relevant information at the right moment, place, and device.

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