Tracking of Moving Objects with 2DPCA-GMM Method and Kalman Filtering
Emadedeen Noureldaim, Mohamed Jedra, Nouredine Zahid · International Journal of Signal Processing Image Processing and Pattern Recognition · 2012
A new method, 2DPCA-GMM of tracking and segmentation in the dynamic environment of objects is proposed in this paper. The method attempts to link the Gaussian mixture model, (GMM) with the method of two dimensional principal component analysis (2DPCA) and apply Kalman Filtering (KF) for tracking. In this context, the aim of the paper is to tackle tracking of moving object based on 2DPCA-GMM together with Kalman prediction of the position and size of object along the image’s sequence. The obtained results successfully illustrate the tracking of a single moving object as well as multiple moving objects based on segmentation generated by 2DPCA-GMM.