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    STUDIA INFORMATICA - Issue no. 2 / 2015  
         
  Article:   OBSTACLE RECOGNITION IN TRAFFIC BY ADAPTING THE HOG DESCRIPTOR AND LEARNING IN LAYERS.

Authors:  . 		 
       
         
  Abstract:   Despite many years of research, obstacle recognition is still a dicffiult, but very important task. We present a multi-class approach, that extracts from images the Histogram of Oriented Gradients (HOG) based on aspect ratio of Region of Interest (ROI) and use them in a multi-class classi fication problem. For the learning phase we propose an original approach based on decision trees. Numerical experiments are performed on a benchmark dataset consisting of animal, pedestrian, car and sign (labeled) images captured in outdoor urban environments and indicate that the proposed model is able to improve the performance of the recognition process.

2010 Mathematics Subject Classi fication. 68T05, 91E45.
Key words and phrases. Multiclass classi cation, HOG, Decision Trees, Boosting.
 
         
     
         
         
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