An Improved Target Detection Algorithm Based On Codebook Model And Research On Tracking Algorithm

The detection and tracking of moving objects have been widely used in real life,such as intelligent video surveillance,intelligent transportation,virtual reality,etc.However,there are many disturbances such as rotation,attitude change,occlusion and motion blur in the application scene,So it is still a challenging task to study the moving object detection and tracking algorithm with high robustness,good accuracy and high real-time performance.The adaptive codebook(SACB)algorithm is a classical background modeling algorithm,which has good effect on target detection and has some resistance to periodic shaking,but there is a "ghost image" And the codeword removal strategy is overly dependent on time and so on.In this paper,an adaptive double codebook detection model is proposed to improve the detection accuracy of the algorithm.The STC algorithm is a representative target tracking algorithm.It is simple,fast and robust.It can track a single target for a long time,but it fails to track the target in scene changes such as attitude change,occlusion or motion blur.In this paper,a weighted spatiotemporal context model and a target position prediction method based on the fusion velocity feature are proposed,which improves the robustness and accuracy of the STC algorithm to a certain extent.The main research work is as follows:1)An improved self-adaptive dual codebook(DECB)algorithm is proposed.By constructing background-foreground dual codebook model with the introduction of a foreground pixel memory layer and moving the pseudo-foreground data which appears frequently into background model in real time,the "empty shadow" is eliminated.To enhance the noise resistance of intensive objects shaking,the pixel is randomly matched with the codebook of its adjacent one,taking the advantage of the similarity of spatiotemporal characteristic among adjacent pixels.The dependence of codeword-clearing strategy on time is reduced by introducing life-style to codeword update mechanism.2)An anti-occlusion processing mechanism and a target loss coping mechanism are proposed.The stability and robustness of the spatial context a priori model are improved by using the weighted partitioning strategy,and the target position is corrected by the velocity feature.If the target is lost,it stops updating the target model,and ensures that the target model is similar to the real model.Avoid obscuring and causing the offset or loss.3)The effectiveness of the two algorithms proposed in this paper is evaluated by experiments.The experimental results are analyzed and compared in detail.