Supervised Appearance Model with Neural Network for Vehicular Object Classification

The object detection is the procedure to evaluate the position of the objects in the given image data by analyzing the template data against the input image. The vehicular object detection involves the feature discovery and location marking in the given image matrix. The fore process may involve the object classification for the determination of the category of the evaluated object in the image matrix. The object classification model is considered as the primary process for the object category determination and discovery. In this paper, the supervised appearance similarity model has been utilized as the template matching scheme for the evaluation object appearance in the input image data. The template matching scheme evaluates the location of all of the discovered objects in the input matrix, which are marked using the blue boxes for the visualization. The deep neural network (DNN) classification model has been utilized for the purpose of category evaluation of the detected objects. The feature minimization in the proposed model has been also achieved using the non-negative matrix factorization (NMF) for the realization of the quick response vehicular detection and classification system. The neural network classification model also utilizes the training data for the vehicular class evaluation. The various performance parameters have been obtained from the proposed model results after the deep evaluation. The experimental results have proved the higher efficiency of the proposed model in comparison with the existing models. The proposed model has obtained 97.25% accuracy which is way higher than the existing models.