Abstract
Multiclass classification is an extensively researched topic due to its importance in making the binary classification problems a complex and well tuned system and minimising the running time for multiple classification problems. In the traffic scenes one can encounter several types of obstacles like cars, pedestrians, animals, low elevated objects, road signs that must be detected and categorised for safety reasons regarding the driver and traffic. The purpose of this paper is two-folds: to accurately classify four obstacle types (pedestrians, cars, animals and other types of objects) and to compare some multiclass classification methods based on Support Vector Machine and Boosting algorithms. The experiments showed that the method Fuzzy Clustering with improvements using Particle Swarm Optimisation achieves great results compared to the traditional hierarchical multiclass classification and the proposed hybrid approach that combines Boosting and Support Vector Machine increases the classification accuracy even further.
Citare
Mocan R., Dioșan L., Support Vector Machine and Boosting based Multiclass Classification for Traffic Scene Obstacles, Studia Universitas Babes-Bolyai, Seria Informatica, 2016, LXI(2):70-81
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