Recognition Algorithm Based On The Goal Of Extracting Feature Information

Object recognition is currently one of the most active research topics in computer vision. After decades of research and development, object recognition technique has been developing fast and its wide use is improving the quality of people's life considerably in various fields. Nowadays, urban transport facilities have been improved and the increasing number of vehicle leads to an increasing accident rate, so intelligent traffic surveillance is imperative. Moreover, traffic object recognition is the groundwork of the intelligent traffic surveillance system. Further research need to be done to improve the object recognition algorithm since it has a significant influence on traffic surveillance.Considering the characteristics of the traffic object, the feature based object recognition algorithm has been studied. Based on these studies, SVM based object recognition algorithm and object recognition algorithms based on keypoint matching are presented.Due to the complexity of the traffic scene and the particularity of the moving object, some kinds of feature extraction methods fit for traffic object have been analyzed. After in-depth study of support vector machine theory and its application principles, SVM based object recognition algorithm is proposed, constructing a SVM based multi-modal classifier and then the traffic object can be recognized. Experimental results show that this method is effective. In addition, the comparison of the recognition accurate for different feature extraction methods provides a basis for feature selection. Furthermore, an object recognition algorithm based Kalman multi-object tracking algorithm is presented. The object recognition algorithm resolves the issue of updating the parameters of Kalman filter. Experimental results show that these modifications further improve tracking performance.However, the results of SVM based object recognition algorithm demonstrate that the effect of object recognition algorithm will be affected by the object detection result. In order to resolve this problem, the local feature extraction algorithm which doesn't depend on object detection result has been analyzed and the object recognition algorithm based on corner feature matching is proposed. SIFT shows great advantage in dealing with the noises and object distortion. Experimental results indicate that object recognition algorithm based on SIFT feature matching is efficient and accurate.