An improved algorithm on viola-jones object detector

In image processing, Viola-Jones object detector is one of the most successful and widely used object detectors. A popular implementation used by most image processing researchers and implementers is the one implemented in OpenCV. The detector shows its strong power in detecting faces, but we found it hard to be extended to other kinds of objects. The convergence of the training phase of this algorithm depends a lot on the training data. And the prediction precision stays low. In this paper, we have come up with new ideas to improve its performance for diverse object categories. We incorporated six different types of feature images into the Viola and Jones' framework. The integral image used by the Viola-Jones detector is then computed on these feature images respectively instead of only on the gray image. In addition, we also integrated a key points based SVM predictor into the prediction phase to improve the confidence of the detection result.