Target detection algorithm and data model optimization based on improved Gaussian mixture model

Abstract

Abstract A set of multi-stage image processing algorithms developed to detect the change of image sequence analysis. This means Moving Target Identification (MTI). The difference compared with the traditional method, the new method of spatial diversity. General purpose of object detection, image processing and image understanding of the specific field of research. Hue Saturation and Value (HSV,) colour space algorithm uses spot and shape detection to ensure detection under various conditions of lighting, shade, and distance. The algorithm is tested on the lighting and form detection account where the different variation of the displays. The only computational process of the challenge group is the presence of more than one target of the same colour in finding the correct target under changing lighting conditions. It has been found that the elasticity target people based on localization of these image processing methods for better detection is compared with the target detection. Digital image data contains most of this image data recognition model is optimized by integrating task planning normalization and inertial representation of the remote sensing image classification model based on spatial communication. The immediate message issue is that the Gaussian compound model cannot detect the entire moving object, and is prone to sudden light changes, etc. The advanced algorithm to be performed is proposed based on the Gaussian compound model and the detection method of the three legal difference moving object. Subsequently, a new adaptive selection technique for Gaussian distributions is introduced to reduce processing time and improve detection accuracy.