Performance analysis of multiclass object detection using SVM classifier

Multiclass object detection is considered for detecting different object classes in a cluttered environment. Traditional approaches require applying a battery of different classifiers to the image with a large number of complex features used to detect the objects. Specialized detectors usually excel in performance, while the class-specific features increase detection accuracy, but at the expense of complexity. In this paper, an efficient method of human face and car detection using cascaded structure of independent object classifiers is proposed. The approach is based on background elimination using statistical features, followed by foreground detection using Principal component analysis (PCA) and Histogram of Gradients (HoG) with SVM classifier. For detecting the object of interest from the image, the system primarily filters the potential object area by analyzing the local histogram distribution. After background elimination, the trained classifier detects foreground using higher order parameters like PCA for human faces and HOG for cars. In this paper, the kernel function for SVM classifier, suitable for individual object classifier is analysed based upon ROC-AUC parameter. The proposed system is implemented in Matlab. The system is validated with performance metrics like precision, recall and accuracy.