Browsing by Author "Wu, Jennifer Yuh-Jen"

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A new vehicle detection approach in traffic jam conditions
(2007)
Wu, Bing-Fei
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Juang, Jhy-Hong
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Tsai, Ping-Tsung
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Chang, Ming-Wei
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Fan, Chung-Jui
The well-known vehicle detectors utilize the background extraction methods to segment the moving objects. The back-ground 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 road surface so that the background information cannot be smoothly updated. Once the traffic is released, the existing background is not suitable for the moving segmentation. The main contribution of this paper is that an efficient vehicle detection approach is proposed to improve the detection accuracy in traffic jam conditions. The land mask decision gives the land information and the merged boundary box rule is presented to realize vehicle detection. The signed square normalized correlation coefficient calculation is addressed, and it is applied to vehicle tracking. The experimental results show that this approach works well in highway and urban area with high accuracy.
Functional Canonical Analysis Between Functional and Interval Data
(2009)
Jou, Yow-Jen
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Huang, Chien-Chia
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Wu, Jennifer Yuh-Jen
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
In this study we discuss the functional canonical correlation analysis between the functional data and the interval data. To address the interval data, a representative is of necessity. Based on the work by Chavent et al. (2002), the representative can be derived by using the Hausdorff distance between intervals. The canonical analysis can be either the mixed functional-multivariate canonical correlation analysis or a pure functional one. This approach is then applied to the roadside vehicle detection by using Radar devices. It can be observed that the weight functions implicitly contain the information about the distances between the specific lane and the detector.
Semi-parametric linear mixed effects model for vehicles identification
(2007)
Jou, Yow-Jen
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Yang, Chai-Tzu
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Huang, Chien-Chia
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Wu, Jennifer Yuh-Jen
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資訊管理與財務金融系註：原資管所+財金所
In order to make the detecting of the lanes and the types of the vehicles traveling on various roadways affordable, radio-frequency (RF) system-on-chip is designed and will be mounted on the roadside to collect vehicle information. We use the Fast Fourier Transform (FFT) to transform the signal of the reflecting wave radar into the numerical data, and utilize it by the statistical approach to discriminate the size of cars and the lanes. In order to classify the types of the vehicles, two models are proposed to model the data. One is multivariate analysis of variance model to account for the main effect and the interaction effect between type and lane, the other is the semi-parametric linear mixed effect model to emphasize the functional characteristic of the data. Both models work well when the number of groups is small but deteriorate when the number of groups increases.
Vehicle Classification using Support Vector Machines and k-means Clustering
(2009)
Cho, Hsun-Jung
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Li, Rih-Jin
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Lee, Hsia
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Wu, Jennifer Yuh-Jen
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運輸與物流管理系註：原交通所+運管所
This investigation involves the combination of support vector machines (SVM) and k-means clustering implemented on radar signal. SVM classifier based on k-means algorithm has an advance for classifying unlabeled data. To classify the vehicle types automatically, the combined method is implemented with radar signals. This paper proposed a classifier training algorithm based on SVM and k-means clustering as follows (i) using the k-means algorithm to label the input feature data extracted from radar signal into two subsets, (ii) train SVM with labeled data, and (iii) classify unidentified radar signal into large vehicle or small vehicle with the trained classifier. Training features of radar signal includes (i) signal volume and (ii) sum of signal variations, both in frequency domain. These features are taken as system input, while vehicle types as system output. The proposed algorithm is implemented and demonstrated with real FMCW radar signals. With the numerical experiment, satisfying result is obtained.