Research On Object Detection Algorithm Of Partially Occluded

Target surveillance and tracking system has a very important prospect in technology and engineering applications. When the backgrounds background is very simple and the target has not been shielding, the traditional algorithm can accurately identify the target. However, when the background is complex and in particular there has movement of the occlusion in the background, traditional detection and tracking strategy often makes bug tracking, and even loss of target. The reality is that the image sequences which were obtained in order to track the target often encounter the situation that target was obscured by other objects. Therefore, this study focuses on solving the occlusion problem in object recognition. At the same time, the use of a translation, rotation and scale invariance of the local shape description methods is the key of occlusion identifying.Image processing technology is the basis of target recognition. Firstly, the basic principles of image processing were introduced in this article, describing image pre-processing theory, including gray-oriented, binarization, median filtering, image smoothing, etc. Also The key technologies of recognition such as feature extraction and feature matching techniques. Were discussed, feature point extraction and matching method were introduced in detail.Secondly, the principles and methods of image matching techniques were discussed. Scale-invariant feature transform (SIFT) algorithm was studied, including the superiority of resistance to image scaling, rotation, distortion and anti-noise, and that the operator for part of the occlusion target recognition.Finally, the neural network pattern recognition method was introduced. Using the improved neural network algorithm to extract the characteristic information and identification of training samples, many cases have been experiment simulation.Experimental results show that using the SIFT operator and the neural network method, occlusion objects are accurately positioned, the scheme also has good robustness against other attacks, such as image zoom, pan, rotate, and anti-noise areas.