Effective Feature Evaluation Method Using Enhanced SVM for Human Detection

Support Vector Machine (SVM) is one of powerful learning machine and has been applied to varying task with generally acceptable performance. The success of SVM for classification tasks in one domain is affected by features which represent the instance of specific class. Given the representative and discriminative features, SVM learning will give good generalization and consequently we can obtain good classifier. In this paper, we will assess the problem of feature choices for human detection tasks and measure the performance of each feature. Here we will consider HOG-family feature. We proposed the multi-scale HOG as a newly family member in this feature group. We also combine SVM with Principal Component Analysis (PCA) to reduce dimension of features and enhance the evaluation speed while retaining most of discriminative feature vectors. The experimental result demonstrates the superiority of the proposed algorithm.