Object tracking using Kalman filter with adaptive sampled histogram

Abstract

An object tracking method based on Kalman filter is proposed in which a novel solution for observation stage is introduced. The new solution gave different weights to different parts of an object and also updates and adapts the reference model for tracking a specific object. The experiments show that the proposed method outperforms the baseline method that uses the histogram as an observation model.