Vehicle Type Recognition Using Probabilistic Constraint Support Vector Machine

The support vector machine (SVM) is one of the most powerful methods in the field of statistical learning theory for constructing a mathematical model in pattern classification. This paper presents a new support vector machine classifier for recognition of vehiche type which has been captured from traffic scene images. A new support vector machine classifier is presented with probabilistic constrains which presence probability of samples in each class is determined based on a distribution function. Noise is caused to found incorrect support vectors thereupon margin can not be maximized. In the proposed method, constraints boundaries and constraints occurrence have probability density functions which it helps for achieving maximum margin. Experimental results in the machine identification shows superiority of the probabilistic constraints support vector machine (PC-SVM) relative to standard SVM.

Keywords:

Pattern recognition , Vehicle type recognition , Machine identification , Support vector machine , Probabilistic constraints