"基于非参数核密度估计和Mean Shift的目标检测与跟踪方法研究"

The research on object detection and tracking based on non-parametric kernel density estimation background modeling and Mean Shift algorithm has been widely utilized in various fields. This paper aims to improve the real-time, robustness, and accuracy of object detection and tracking by proposing new methods. The main contributions of this paper include: 1. To address the computational time issue in traditional non-parametric kernel density estimation background modeling, a method based on regional information and region smoothing is proposed to reduce the calculation complexity without affecting the detection results, thereby improving the real-time performance of the algorithm. In the subsequent noise reduction process, the connectivity between neighboring pixels is utilized to effectively suppress noise while preserving detailed segmentation information. 2. In traditional Mean Shift-based tracking algorithms, tracking performance may suffer when the foreground target is similar to the background. To overcome this limitation, a method based on improved backward projection image is proposed on the basis of traditional point sample estimation for calculating the backward projection image. Experimental results demonstrate that this algorithm effectively enhances the accuracy and robustness of object tracking. In conclusion, the research on object detection and tracking based on non-parametric kernel density estimation background modeling and Mean Shift algorithm has shown promising results in improving the real-time performance, robustness, and accuracy of object detection and tracking. The proposed methods provide valuable insights for further advancements in the field of computer vision and artificial intelligence.