Multiple objects tracking using Extended Kalman Filter, GMM and Mean Shift Algorithm - A comparative study

Object tracking is a primary step for image processing applications like object recognition, navigation systems and surveillance systems. The current image and the background image is differentiated by approaching conventionally in image processing. Image subtraction based algorithms are mainly used in extracting features of moving objects and take the information in frames. Here three algorithms namely Extended Kalman Filter, Gaussian Mixture Model (GMM), Mean Shift Algorithm are compared in the context of multiple object tracking. The comparative results show that GMM performs well when there are occlusions. Extended Kalman filter fails because of abnormal behavior in the distribution of random variables when there is nonlinear transformation. It cannot identify multiple objects when there are occlusions. Mean shift algorithm is best suitable for single object tracking and is very sensitive to window size which is adaptive. Results show that this algorithm has the limitation to detect multiple objects when there is even slight occlusion.