Adjustment of Surveillance Video Systems by a Performance Evaluation Function

This paper proposes an evaluation metric for assessing the performance of a video tracking system. This metric is applied to adjust the parameters that regulates the video tracking system in order to improve the system perfomance. Thus, the automated optimization method is based on evolutionary computation techniques. The illustration of the process is carried out using three very different video sequences in which the evaluation function assesses trajectories of airplanes, cars or baggagetrucks in an airport surveillance application

Ein Modell zur Analyse der Umwandlungskapazitaeten im Energiesektor der Bundesrepublik Deutschland Dokumentation. Stand: Dezember 1983

TIB: RN 5906 (237)+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

Zur Einbettung der Energiewirtschaft in die Volkswirtschaft Vier dynamische Modelle

SIGLEDEGerman

PARPROG - Parallele Prognoseverfahren fuer die Absatzplanung Schlussbericht

1. Current scientific/technical situation: Currently, there are two major approaches to forecasting of economical data: classical statistical methods and Computational Intelligence (CI) approaches (mainly Neural Networks). While the statistical methods depend on expert knowledge in case of non-linear application-specific models, Neural Nets lack the feature of explainability. 2. Motivation/objective of the investigation: Synthesis of statistical and parallel CI methods aiming at an application-specific forecasting tool with explainable results. Due to the high requirements of computation time, efficient parallel implementations are required. 3. Method: Parameter estimation of application-specific statistical forecasting models by means of parallel Evolutionary Algorithms (EAs). Learning of transformations of multi-variate impact time series by means of parallel Genetic Programming (CP). 4. Results: Multi-variate time series models can be successfully estimated. The results concerning the fitting of the estimated data in the past are better than those achieved by expert knowledge and heuristics. The plausibility of the results can be enhanced by restricting the parameter space based on expert knowledge. Due to overfitting, the parallel CP did not improve the explainability of the results. The parallelization of Eas using a modified neighborhood model turned out to be robust and scalable with respect to available communicational and computational capacities. 5. Conclusions/application possibilities: The combination of application-specific models and parallel CI methods can be applied to arbitrary statistical estimation problems. By integrating expert knowledge, this approach leads to a high acceptance of new developments in computer science. Since the parallelization is scalable, the methods are suitable even for small PC networks. (orig.)SIGLEAvailable from TIB Hannover: F99B1154 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekBundesministerium fuer Bildung und Forschung (BMBF), Bonn (Germany)DEGerman