Abstract
Several single classifiers have been proposed to recognize objects in images. Since this approach has restrictions when applied in certain situations, one has suggested some methods to combine the outcomes of classifiers in order to increase overall classification accuracy. In this sense, we propose an effective method for a frame-by-frame classification task, in order to obtain a trade-off between false alarm decrease and true positive detection rate increase. The strategy relies on the use of a Class Set Reduction method, using a Mamdani fuzzy system, and it is applied to recognize pedestrians and vehicles in typical cybercar scenarios. The proposed system brings twofold contributions: i) overperformance with respect to the component classifiers and ii) expansibility to include other types of classifiers and object classes. The final results have shown the effectiveness of the system.
This work is supported in part by Fundação para a Ciência e Tecnologia (FCT) de Portugal, under Grant POSC/EEA-SRI/58279/2004, and by CyberC3 project (European Asia IT&C Programme). Luciano Oliveira is supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), programme of Ministry of Education of Brazil, scholarship no BEX 4000-5-6.
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Oliveira, L., Monteiro, G., Peixoto, P., Nunes, U. (2007). Towards a Robust Vision-Based Obstacle Perception with Classifier Fusion in Cybercars. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2007. EUROCAST 2007. Lecture Notes in Computer Science, vol 4739. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75867-9_136
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DOI: https://doi.org/10.1007/978-3-540-75867-9_136
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