By Lauro Snidaro, Ingrid Visentini (auth.), Paolo Remagnino, Dorothy N. Monekosso, Lakhmi C. Jain (eds.)
This ebook is a continuation of our past volumes on suggestions in Defence help platforms. This publication contains a pattern of contemporary advances in clever tracking. The contributions include:
· facts fusion in smooth surveillance
· disbursed clever surveillance platforms modeling for functionality evaluation
· Incremental studying on trajectory clustering
· Pedestrian velocity profiles from video sequence
· System-wide monitoring of individuals
· A scalable method in accordance with normality parts for clever surveillance
· allotted digital camera overlap estimation
· Multi-robot staff for environmental monitoring
The publication is directed to the protection specialists, engineers, scientists, scholars and professors who're attracted to clever monitoring.
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Additional resources for Innovations in Defence Support Systems – 3: Intelligent Paradigms in Security
The control center C is modeled considering: • a processing function TC (t), • a database DC , • a communication unit CC . 5 Performance Evaluation of Multi-sensor Architectures The Performance evaluation of data fusion systems, such as multi-sensors surveillance systems is usually done considering only the information processing and refinement algorithms. Much work has been dedicated in the recent years to evaluate the performances of surveillance algorithms usually by a comparison of the output of those algorithms with a manually obtained ground truth.
Sensor Scheduling Depending on the planning and requests coming from Resource Planning, this level is in charge of determining a detailed schedule of commands for each sensor. e. g. robots) and multimode (day/night cameras, multi-frequency radar) sensors. The problem of sensor scheduling has been addressed in , and a recent contribution on the scheduling of visual sensors can be found in . Sensor Control This is the lowest level and possibly also the simplest. The purpose of this level is to optimize sensor parameters given the current commands imposed by Level 1 and 2.
ACIVS 2008. LNCS, vol. 5259, pp. 775–784. : An ensemble based data fusion approach for early diagnosis of alzheimer’s disease. : Surveillance camera scheduling: A virtual vision approach. : A fusion method that performs better than best sensor. In: Proceedings of the First International Conference on Multisource-Multisensor Information Fusion, pp. : Constructing boosting algorithms from svms: An application to one-class classification. : Scanning the issue / technology - Special Issue on Video Communications, processing and understanding for third generation surveillance systems.
Innovations in Defence Support Systems – 3: Intelligent Paradigms in Security by Lauro Snidaro, Ingrid Visentini (auth.), Paolo Remagnino, Dorothy N. Monekosso, Lakhmi C. Jain (eds.)