Title: A New Approach to Video-Based Traffic Surveillance Using Fuzzy Hybrid Information Inference Mechanism
Authors: Wu, Bing-Fei
Kao, Chih-Chung
Juang, Jhy-Hong
Huang, Yi-Shiun
Institute of Electrical and Control Engineering
Keywords: Congested condition;traffic surveillance;vehicle detection;vehicle tracking
Issue Date: 1-Mar-2013
Abstract: 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.
URI: http://dx.doi.org/10.1109/TITS.2012.2208190
ISSN: 1524-9050
DOI: 10.1109/TITS.2012.2208190
Volume: 14
Issue: 1
Begin Page: 485
End Page: 491
Appears in Collections:Articles

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