Multiple-kernel adaptive segmentation and tracking (MAST) for robust object tracking

Zheng Tang, Jenq Neng Hwang, Yen Shuo Lin, Jen-Hui Chuang

研究成果: Conference contribution同行評審

17 引文 斯高帕斯(Scopus)

摘要

In a video surveillance system with static cameras, object segmentation often fails when part of the object has similar color with the background, resulting in poor performance of the subsequent object tracking. Multiple kernels have been utilized in object tracking to deal with occlusion, but the performance still highly depends on segmentation. This paper presents an innovative system, named Multiple-kernel Adaptive Segmentation and Tracking (MAST), which dynamically controls the decision thresholds of background subtraction and shadow removal around the adaptive kernel regions based on the preliminary tracking results. Then the objects are tracked for the second time according to the adaptively segmented foreground. Evaluations of both segmentation and tracking on benchmark datasets and our own recorded video sequences demonstrate that the proposed method can successfully track objects in similar-color background and/or shadow areas with favorable segmentation performance.

原文English
主出版物標題2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1115-1119
頁數5
ISBN(電子)9781479999880
DOIs
出版狀態Published - 18 五月 2016
事件41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
持續時間: 20 三月 201625 三月 2016

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2016-May
ISSN(列印)1520-6149

Conference

Conference41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
國家China
城市Shanghai
期間20/03/1625/03/16

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