Fusion of local and global features for efficient object detection

In this paper, we propose a two-stage method for efficient object detection that combines full advantages of AdaBoost and SVM to achieve a reasonable balance in both training and classification speed. In the first stage, we use Haar wavelet features and AdaBoost to train a cascade of classifiers for quick and efficient rejection. This cascade of classifiers consists of simple-to-complex classifiers that allow adapting to complexity of input patterns, rejects almost 90%-99% non-object patterns rapidly. Hard patterns, object-like patterns, which are passed through the first stage, will be classified by the second stage which uses a non linear SVM-based classifier with pixel-based global features. The nonlinear SVM classifier is robust enough in order to reach high performance. We have investigated our proposed method to detect different kinds of objects such as face and facial features like eye and mouth regions. In training, our system is roughly 25 times faster than the system trained by AdaBoost. In running, the experimental results show 1,000 times faster than SVM based method and slightly slower than AdaBoost based method with a comparable accuracy.