Object matching using hybrid modified RGB color model and HRR-based background detection

Jing Ming Guo*, Yang Chen Tian, Jiann Der Lee

*此作品的通信作者

研究成果: 圖書/報告稿件的類型會議稿件同行評審

1 引文 斯高帕斯(Scopus)

摘要

This study presents a pixel-wise statistical approach to distinguish every pixel into foreground, background, shadow or highlight for background subtraction and foreground detection. The modified RGB color model is proposed to effectively reduce misclassifying the foreground pixels into highlights. The modified Highest Redundancy Ratio (HRR)-based background update method is also proposed to overcome the lighting variation and slow motion object problems in background reconstruction. In tracking procedure, a decision function with low computational complexity is proposed to sequentially evaluate the objects' correlation between consecutive frames. The decision function consists of the objects' centroid distances, objects' area differences, and objects' overlapping areas between current frame and previous frame. As documented in experimental results, the proposed method can achieves high matching rate, which is great advantageous in surveillance systems. Index Terms-Object matching, object tracking, surveillance system, foreground detection, shadow removal, RGB color model, HRR algorithm.

原文英語
主出版物標題Proceedings - 34th Annual Conference of the IEEE Industrial Electronics Society, IECON 2008
發行者IEEE Computer Society
頁面2992-2997
頁數6
ISBN(列印)9781424417667
DOIs
出版狀態已出版 - 2008

出版系列

名字IECON Proceedings (Industrial Electronics Conference)

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