Integration of new moving object segmentation and classification techniques using optimal salp swarm-based feature fusion with linear multi k-SVM classifier

Abstract

The feature extraction technique is applied on least enclosing rectangle (LER) of the segmented object to increase the processing speed. The main intuition of this salp swarm algorithm relays on reducing the computational load of the proposed classifier by removing the repetitive and unrelated features from the feature vector. Also, increased training samples of similarly shaped classes when applied on the classifier can generate the misclassification results. Thus, a new layered kernel-based support vector machine (k-SVM) classifier is developed by means of integrating the k-neural network classifier and layered SVM classifier. Because of the high dimensional features, a difficulty occurs in the application of a single classifier. In order to ease the computational load, this multi classifier is integrated with a shadow elimination technique to classify the object categories of intelligent transportations system such as motorcycles, bicycles, cars, and pedestrians.