Moving Average for Error Mitigation in Pedestrian Tracking - Komati, Karin Satie;Bianchi, Gabriel Luiz
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The Moving Average is very common in the area of time series analysis, mainly for analysing changes in stock market values. In this work, the Moving Average will be used in another context, to mitigate errors in pedestrian tracking results in video surveillance. The HOG feature extraction technique and the SVM classifier were used to detect pedestrians. For our analysis, we used the database provided by the University of Edinburgh's CAVIAR (Context Aware Vision using Image-based Active Recognition) project. The results were positive, especially when the pedestrians moved vertically in the scene in the database used.…mehr

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Produktbeschreibung
The Moving Average is very common in the area of time series analysis, mainly for analysing changes in stock market values. In this work, the Moving Average will be used in another context, to mitigate errors in pedestrian tracking results in video surveillance. The HOG feature extraction technique and the SVM classifier were used to detect pedestrians. For our analysis, we used the database provided by the University of Edinburgh's CAVIAR (Context Aware Vision using Image-based Active Recognition) project. The results were positive, especially when the pedestrians moved vertically in the scene in the database used.
Autorenporträt
Karin Satie Komati enseigne à l'Ifes Campus Serra depuis 2012. Elle est titulaire d'un diplôme en informatique et en génie électrique, d'un master en informatique et d'un doctorat en génie électrique, tous obtenus à l'UFES. Gabriel Luiz Bianchi est titulaire d'une licence en systèmes d'information de l'Ifes Campus Serra.