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arXiv 2017年

作者： Dittrich, Fabio De Oliveira, Luiz E. S. Britto, Alceu S. Koerich, Alessandro L. Laboratoire d'Imagerie Vision et Intelligence Artificielle Ecole de Technologie Superieure 1100 Notre-Dame West MontrealQCH3A 1E3 Canada Federal University of Paraná Department of Informatics Centro Politécnico Curitiba PR81531-970 Brazil Pontifical Catholic University of Paraná Postgraduate Program in Computer Science R. Imaculada Conceiç aõ 1155 Curitiba PR80215-901 Brazil Ecole de Technologie Supérieure Department of Software and IT Engineering 1100 Notre-Dame West MontrealQCH3A 1E3 Canada

This paper presents two novel approaches for people counting in crowded and open environments that combine the information gathered by multiple views. Multiple camera are used to expand the field of view as well as to mitigate the problem of occlusion that commonly affects the performance of counting methods using single cameras. The first approach is regarded as a direct approach and it attempts to segment and count each individual in the crowd. For such an aim, two head detectors trained with head images are employed: One based on support vector machines and another based on Adaboost perceptron. The second approach, regarded as an indirect approach employs learning algorithms and statistical analysis on the whole crowd to achieve counting. For such an aim, corner points are extracted from groups of people in a foreground image and computed by a learning algorithm which estimates the number of people in the scene. Both approaches count the number of people on the scene and not only on a given image or video frame of the scene. The experimental results obtained on the benchmark PETS2009 video dataset show that proposed indirect method surpasses other methods with improvements of up to 46.7% and provides accurate counting results for the crowded scenes. On the other hand, the direct method shows high error rates due to the fact that the latter has much more complex problems to solve, such as segmentation of heads. Copyright © 2017, The Authors. All rights reserved.

关键词： Cameras

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Empirical Studies on Application of Genetic Algorithms and Ant Colony Optimization for Data Clustering

Empirical Studies on Application of Genetic Algorithms and A...

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computer Science Society (SCCC) International Conference Chilean FLAGGED

作者： Thelma Elita Colanzi Wesley Klewerton Guez Assunção Aurora Trinidad Ramirez Pozo Ana Cristina B. Kochem Vendramin Diogo Augusto Barros Pereira Computer Science Postgraduate Program Federal University of Paraná (UFPR) Curitiba Paraná Brazil Graduate School of Electrical Engineering and Computer Science (CPGEI) Federal Technological University of Paraná (UTFPR) Curitiba Paraná Brazil Informatics Department (DAINF)

Cluster analysis is used in several research areas to classify data sets in groups by their similar characteristics. Metaheuristic-based techniques, such as Genetic Algorithms (GAs) and Ant Colony Optimization (ACO), have been applied in order to increase the clustering algorithm performance. GA and ACO-based clustering algorithms are capable of efficiently and automatically forming natural groups from a pre-defined number of clusters. This paper presents a GA and an ACO algorithm to the clustering problem. Both algorithms were refined using local search in order to improve the clustering accuracy. The results are compared on numeric UCI databases.

关键词： Clustering algorithms Search problems Equations Mathematical model Gallium Genetic algorithms Memetics

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