Generic Object Detection Using Improved Gentleboost Classifier

Object detection in natural scene and image is playing an important role in computer vision. The traditional object detection method is more about local feature extraction and supervised learning. Using this method, the detection rate of the image with complex scenes is low. But the reality scene is complex and the real-time detection system need to handle a large number of images. In order to remedy the deficiency of the traditional test methods in the object detection, we propose a new approach which uses patches of object and its relative position with the object's center as the feature and a new improved gentleBoost classifier, which enables it to work with better detection result. In this method, we use the linear regression stump as the weak classifier in learning algorithm, weights to the prediction model from the positive and negative classification, but not to weight it only from the positive aspect. At the same time, we compare our proposed algorithm with the detection method proposed by Andreas et al. in Ref 9] and also compares results with different number of weak learners. Experimental results show that our algorithm is simple to implement and the positive detection rate is high. (C) 2012 Published by Elsevier B.V. Selection and/or peer-review under responsibility of Garry Lee