Research On Moving Object Classification In Video Surveillance

As an emerging field of computer vision research direction, intelligent video surveillance is widely used in military and civil application. Intelligent video surveillance aims at detecting, tracking, identifying moving objects and understanding objects behaviors through analysis and processing image sequences. Objects classification is an important aspect of intelligent video surveillance whose research content is to classify moving objects into semantically meaningful categories and provide information for tracking or understanding objects behaviors.The moving object classification of normal outdoor scenes based on static odd-camera is studied in this paper. The accuracy of objects classification is determined by the quality of the gather information. But when the information is being collected in the outdoor, it will be affected by illumination changes,occlusion,weather and other external factors. The change of the position and angle of the objects also affect information collecting. In order to classify objects into right classes and achieve real-time in such a complex external environment, the features that most able to reflect the nature of the objects should be chose, the speed and the performance of the classifier also should be increased.Considering these two aspects, the main work of this paper is as follows:1. A new method for feature selection is proposed. Firstly, many features are extracted from the objects. Then this new method is used to evaluate the performance of each feature, and form a good sub-set of features for classification. After using this method for feature selection, the speed of the classifier is greatly improved under the premise of ensuring the classification accuracy.2. Local Binary Pattern is improved, and used for objects classification. In recent years, local features are more concerned because of its robust. Local features are widely used in objects recognition and image matching. Because of the large amount of calculation and high demanding of video resolution, it is rarely used for objects classification. LBP is an effective texture descriptor, and its calculation is very simple. LBP is used for objects classification in this paper, and the result is good.3. Adaboost algorithm, as a classical algorithm in machine learning, is used for objects classification in video surveillance. And it is used for multi-class problem. The Adaboost algorithm not only forms a strong classifier, but also selects features. When LBP is used for classification, there is large number of LBP features. The Adaboost algorithm evaluates the performance of each feature, and selects features to form a strong classifier. The speed and performance of the classification are improved.