Aerial video image object detection and tracing based on motion vector compensation and statistic analysis

Object detection in aviation video is very important in many occasions. Through research on characteristic of aviation video image, and analyzing of motion estimation theory, an object detection technique based on motion vector compensation and statistical analysis is proposed. Through effective image preprocessing, noise that influenced the capacity of algorithm is removed through a Bayesian estimation based wavelet denoising method; a compensation is carry out between the motion vector computed from parameters of camera and the motion estimation that usually used in all kinds of image compression algorithm. The compensation makes it easy to distinguish object motion from background. Then statistic analysis, a clustering using center and density of compensated motion vector is performed to eliminate isolate motion vector and realize object detection. Settings of the threshold can prevent some errors of global estimation which derived from the parameters from compensation, and have some accessorial effect in motion vector clustering and object detection.