Support vector regression for surveillance purposes
This paper addresses the problem of applying powerful statistical pattern classification algorithm based on kernel functions to target tracking on surveillance systems. Rather than directly adapting a recognizer we develop a localizer directly using the regression form of the Support Vector Machines (SVM). The proposed approach considers to use dynamic model together as feature vectors and makes the byperplane and the support vectors follow the changes in these features. The performance of the tracker is demonstrated in a sensor network scenario with a constant velocity moving target on a plane for surveillance purpose.