Target Detection Method Based On Visual Attention Mechanism

The ability, hawk can keenly catch and track the quarry in complex background, has been the pursuiting aim of intelligent tracking system. It is alaways necessary for moving target detection and tracking to extract static features and dynamic features by simulating visual neural information processing system through from-bottom-to-up target attention. As static features are often the basis of the dynamic features, this thesis mainly studies static features extraction and target recognition in complex background, and then texture feature is integrated into visual perception calculation model to improve the salience of interested target in saliency map, so as to firstly focus on interest targets and quickly detect target.Firstly, the current development about visual attention model is introduced and the structure and features of the human perception system is discussed. Saliency map is the basis of visual attention, so it is a way for realizing the target detection to consciously enhance the salience of interested targets in the saliency map. Because the Itti visual perception model lacks the features classification, it is difficult to focus on interested target. A novel visual perception model is proposed to improve the salience of interested target based on texture extraction and classification in this thesis.Secondly, texture feature is extracted by using the Graylevel Co-occurrence Matrix(GLCM) and is classified as natural or man-made object by Support Vector Machine(SVM). There are texture feature differences between natural objects and the man-made objects. The texture feature of the natural objects is always rough and that of man-made objects is mild.Finally, A novel visual perception model is proposed to improve the salience of interested target based on texture extraction and classification, and test results are presented. This model can provide more clues to distinguish interested target from natural background. Theoretical and experimental results show that the improved model can enhance the salience of the interested targets in saliency map, hence to detect target in the complex background.