Multi-technique object tracking approach- A reinforcement paradigm. (February 2018)

• Record Type: Journal Article
• Authors: Samuel, Oluwarotimi Williams; Asogbon, Grace Mojisola; Sangaiah, Arun Kumar; Li, Guanglin
• Is Part Of: Computers & electrical engineering. Volume 66(2018)
• Page Start: 557
• Page End: 568
• Publication Date: 2018-02
• Subjects: Object tracking -- Surveillance -- CamShift -- Unscented particle filter -- Corrected background weighted histogram; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.02.002
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: In recent years, surveillance is fast becoming an essential part of everyday life in both commercial and residential environments due to the vital role it plays in the society. The existing tracking methods are limited by factors such as illumination variations, occlusions, camera movements, and background clutters among others. Hence, a robust multi-technique tracking method based on continuous adaptive mean shift (CamShift), corrected background weighted histogram, and unscented particle filter techniques is proposed. The mean square error statistic was used to evaluate the performance of the proposed method in terms of correctly estimating the path of a target object in video sequence acquired from three different scenarios. The obtained results showed that our proposed method has a significant improvement both quantitatively and qualitatively across all scenarios compared to the traditional CamShift and some existing methods. The proposed multi-technique approach might potentially improve the tracking capability of surveillance devices.