Jhy-Hong Juang

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In this paper, a vehicle detection approach for complex environments is presented. This paper proposes methods for solving problems of vehicle detection in traffic jams and complex weather conditions such as sunny days, rainy days, cloudy days, sunrise time, sunset time, or nighttime. In recent research, there have been many well-known vehicle detectors(More)
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(More)
The well-known vehicle detectors utilize the background extraction methods to segment the moving objects. The background updating concept is applied to overcome the luminance variation which results in the error detection. These systems will meet a challenge when detecting the vehicles in the traffic jam conditions at sunset. The vehicles will cover the(More)
Vision-based vehicle detector systems are becoming increasingly important in ITS applications. Real-time operation, robustness, precision, accurate estimation of traffic parameters, and ease of setup are important features to be considered in developing such systems. Further, accurate vehicle detection is difficult in varied complex traffic environments.(More)
In this study, an real-time multiple-vehicle detection and tracking system in complex environments with automatic lane detection and reducing shadow effects is proposed. First, lane marks can be automatically detected, and this automation makes the proposed system more possible to deploy in the practical traffic conditions. Second, Histogram Extension (HE)(More)
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