The Research Of Container Detection Methods Based On Adaboost

Currently, using computer vision technology is the direction of container recognition and position in automatic container loading and unloading operations.Two of literatures, surveys and research papers concerning up-to-date techniques of container detection are read and analyzed. Putting up the Container Handling Model System(CHMS), compiling the code in Windows, and carrying out simulations about the two methods on the CHMS. The experiments indicate that the methods of container detection proposed in showing a certain degree of theoretical and practical value. Paper mainly includes the following several aspects:1.Using container detection method based on AdaBoost learning algorithm, which selects few key haar-like features from a large set of features, to build a robust cascade classifier. The convergence and generalization capability of AdaBoost algorithm as well as the effect of weight-updating approach on the classifier's performance are analysed comprehensively. This paper used the sample storehouse which one founded to carry on the training and get the container classifier. Detecting the container model In a variety of backgrounds and lighting conditions used the classifier. The experimental results show that the effect of container classifier is suitable to detection.2. Providing an approach to update the template based on a Kalman filter. Intruducing the the basic principles of the template matching based on the color histogram and the Kalman filter, and giving the specific algorithm of the color histogram. The Kalman filter update three component of each pixel value and get the optimum template, and containers are detected by the optimum template. This method improves the accuracy and the stability of template matching.