A new approach to video-based traffic surveillance using fuzzy hybrid information inference mechanism

Bing-Fei Wu, Chih Chung Kao, Jhy Hong Juang, Yi Shiun Huang

研究成果: Article同行評審

20 引文 斯高帕斯(Scopus)

摘要

This study proposes a new approach to video-based traffic surveillance using a fuzzy hybrid information inference mechanism (FHIIM). The three major contributions of the proposed approach are background updating, vehicle detection with block-based segmentation, and vehicle tracking with error compensation. During background updating, small-range updating is adopted to overcome environmental changes under congested conditions. During vehicle detection, the proposed approach detects the vehicle candidates from the foreground image, and it resolves problems such as headlight effects. The tracking technique is employed to track vehicles in consecutive frames. First, the method detects edge features in congested scenes. Next, FHIIM is employed to determine the tracked vehicles. Finally, a method that compensates for error cases under congested conditions is applied to refine the tracking qualities. In our experiments, we tested scenarios both inside and outside the tunnel with three lanes. The results showed that the proposed system exhibits good performance under congested conditions.

原文English
文章編號6264098
頁(從 - 到)485-491
頁數7
期刊IEEE Transactions on Intelligent Transportation Systems
14
發行號1
DOIs
出版狀態Published - 1 1月 2013

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