Object Tracking Based on Corrected Background-Weighted Histogram Mean Shift and Kalman Filter

Abstract

The classical mean shift (MS) algorithm is the best color-based method for object tracking. However, in the real environment it presents some limitations, especially under the presence of noise, objects with partial and full occlusions in complex environments. In order to deal with these problems, this paper proposes a reliable object tracking algorithm using corrected background-weighted histogram (CBWH) and the Kalman filter (KF) based on the MS method. The experimental results show that the proposed method is superior to the traditional MS tracking in the following aspects: 1) it provides consistent object tracking throughout the video; 2) it is not influenced by the objects with partial and full occlusions; 3) it is less prone to the background clutter.

A Robust Framework for Accurate Face Tracking

“…Bhattacharyya coefficient based similarity measurement could not reflect the change of object size accurately. The background-weighted histogram (BWH) mainly aims to decrease background interference in target representation and that it is proposed in [17], [18] is somewhat incorrect and it is proved in [19]. The corrected background-weighted histogram (CBWH) is proved to reduce the interference of background in target localization as proposed in [19], [20].…”

“…CBWH mainly reduce the prominent background features only in the target model but not in the target candidate model. CBWH was explained with details in [19], [20].…”

A robust framework for object tracking based on corrected background-weighted histogram mean shift and unscented Kalman filter

“…And the corrected background-weighted histogram (CBWH) is proposed, which can truly achieve what the original BWH method wants: reduce the interference of background in target localization, Therefore, in [10]. This paper proposes a robust framework for object tracking under complex environment due to partial and full occlusion as well as background variation.…”