A Sampling-Resampling Based Bayesian Learning Approach for Object Tracking
Abhishek Singh, Padmini Jaikumar, Suman Kumar Mitra · 2008
This paper proposes an effective background subtraction technique in still camera videos, to track objects with high degree of sensitivity, accuracy and low false detections. The method involves applying a Bayesian learning technique to update parameters of clusters formed by pixel observations at a particular spatial position. The proposed method also overcomes the limitation of having a heuristically fixed number of clusters in existing tracking techniques which are based on mixture modeling of background. The results favourably compare with some existing methods for a variety of test videos, including those having very low object-background contrast.