Object Tracking Approach based on Mean Shift Algorithm

Abstract

Object tracking has always been a hotspot in the field of computer vision, which has a range of applications in real world. The object tracking is a critical task in many vision applications. The main steps in video analysis are: detection of interesting moving objects and tracking of such objects from frame to frame. Most of tracking algorithms use pre-defined methods to process. In this paper, we introduce the Mean shift tracking algorithm, which is a kind of important no parameters estimation method, then we evaluate the tracking performance of Mean shift algorithm on different video sequences. Experimental results show that the Mean shift tracker is effective and robust tracking method

Face tracking: State of the art

“…In Results of [28], Zhang have obtain that the mean shift tracking algorithm will be robust under these conditions with varying viewpoints and illumination changes. In the David image sequence, the illumination is varying from weak to be strong, as well as the head has a great angle rotation.…”

“…Most recently, several modular systems using a combination of shape analysis, color segmentation and motion information for locating or tracking heads and faces in an image sequence have been developed [27,28,29,30,31].…”

Impulsive Neural Networks Algorithm Based on the Artificial Genome Model

“…Since the system guides single cell division and changes the cell connection to generate the neural network by development instruction, so it is called cell encoding algorithm [5]. For some existing problems on cell encoding algorithm, Luke and Spector put forward edge coding algorithm, and its network growth process mainly modifies by the edge of network rather than the nodes [5][6][7][8]. Recently, Suchorzewski gave a developmental symbol coding algorithm which is similar to cell encoding, scalable and modular self-adaptive neural network are evolved [9].…”