The study of a real-time environment adaptive vehicle detection in complex traffic conditions for remote monitoring video systems

• Main Authors: Ping-Tsung Tsai, 蔡秉宗
• Other Authors: Bing-Fei Wu
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/90921151998530181421

碩士 === 國立交通大學 === 電機與控制工程系所 === 96 === Vehicle detectors utilize the background extraction methods to segment the moving objects. The background updating concept is applied to overcome the luminance variation which always results in the error object detection. These systems will be challenged to detect the vehicles in traffic jams or in different luminance conditions. Since vehicles will cover the road surface or move slowly in traffic jams, the background is difficult to be converged or updated. Once the traffic is released, the existed background is unsuitable for the follow-up moving object segmentation. In this thesis, an adaptive vehicle detection approach is proposed to improve the detection accuracy in traffic jams. And a histogram extension method is utilized to segment moving objects robustly with luminance adaptation. Furthermore, the vehicle detection based on merged boundary box rule method is a good solution to reconstruct the broken moving contour for enhancing the detection accuracy. And the moving object segmentation method based on the histogram extension process is presented. Besides that, the detection system detects the vehicles from the image sequence which captured from the CCD camera on the road side and transmitted via network real time after compressed.