Multiple Tracking of Moving Objects with Kalman Filtering and PCA-GMM Method

• 作者: Emadeldeen Noureldaim Mohamed Jedra Nouredine Zahid
• 来源期刊: 智能信息管理(英文)
• 学科: 工学
• 关键词: COMPONENT PIXELS GAUSSIAN Mixture MODEL Principle COMPONENT Analysis Background MODEL Noise Process Segmentation TRACKING KALMAN Filtering
• 年，卷（期）: 2013,（2）
• 页码范围: 42-47
• 页数: 6页
• 分类号: TP39

摘要

In this article we propose to combine an integrated method, the PCA-GMM method that generates a relatively improved segmentation outcome as compared to conventional GMM with Kalman Filtering (KF). The combined new method the PCA-GMM-KF attempts tracking multiple moving objects;the size and position of the objects along the sequence of their images in dynamic scenes. The obtained experimental results successfully illustrate the tracking of multiple moving objects based on this robust combination