Browsing by Author "Shou, Yu-Wen"

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A Conditional Entropy-Based Independent Component Analysis for Applications in Human Detection and Tracking
(2010)
Lin, Chin-Teng
;
Siana, Linda
;
Shou, Yu-Wen
;
Shen, Tzu-Kuei
;
電控工程研究所
We present in this paper a modified independent component analysis (mICA) based on the conditional entropy to discriminate unsorted independent components. We make use of the conditional entropy to select an appropriate subset of the ICA features with superior capability in classification and apply support vector machine (SVM) to recognizing patterns of human and nonhuman. Moreover, we use the models of background images based on Gaussian mixture model (GMM) to handle images with complicated backgrounds. Also, the color-based shadow elimination and head models in ellipse shapes are combined to improve the performance of moving objects extraction and recognition in our system. Our proposed tracking mechanism monitors the movement of humans, animals, or vehicles within a surveillance area and keeps tracking the moving pedestrians by using the color information in HSV domain. Our tracking mechanism uses the Kalman filter to predict locations of moving objects for the conditions in lack of color information of detected objects. Finally, our experimental results show that our proposed approach can perform well for real-time applications in both indoor and outdoor environments.
An Efficient and Robust Moving Shadow Removal Algorithm and Its Applications in ITS
(2010)
Lin, Chin-Teng
;
Yang, Chien-Ting
;
Shou, Yu-Wen
;
Shen, Tzu-Kuei
;
電控工程研究所
We propose an efficient algorithm for removing shadows of moving vehicles caused by non-uniform distributions of light reflections in the daytime. This paper presents a brand-new and complete structure in feature combination as well as analysis for orientating and labeling moving shadows so as to extract the defined objects in foregrounds more easily in each snapshot of the original files of videos which are acquired in the real traffic situations. Moreover, we make use of Gaussian Mixture Model (GMM) for background removal and detection of moving shadows in our tested images, and define two indices for characterizing non-shadowed regions where one indicates the characteristics of lines and the other index can be characterized by the information in gray scales of images which helps us to build a newly defined set of darkening ratios (modified darkening factors) based on Gaussian models. To prove the effectiveness of our moving shadow algorithm, we carry it out with a practical application of traffic flow detection in ITS (Intelligent Transportation System)-vehicle counting. Our algorithm shows the faster processing speed, 13.84 ms/frame, and can improve the accuracy rate in 4% similar to 10% for our three tested videos in the experimental results of vehicle counting.
Multiclient Identification System Using Adaptive Probabilistic Model
(2010)
Lin, Chin-Teng
;
Siana, Linda
;
Shou, Yu-Wen
;
Yang, Chien-Ting
;
電控工程研究所
This paper aims at integrating detection and identification of human faces in a more practical and real-time face recognition system. The proposed face detection system is based on the cascade Adaboost method to improve the precision and robustness toward unstable surrounding lightings. Our Adaboost method innovates to adjust the environmental lighting conditions by histogram lighting normalization and to accurately locate the face regions by a region-based-clustering process as well. We also address on the problem of multi-scale faces in this paper by using 12 different scales of searching windows and 5 different orientations for each client in pursuit of the multi-view independent face identification. There are majorly two methodological parts in our face identification system, including PCA (principal component analysis) facial feature extraction and adaptive probabilistic model (APM). The structure of our implemented APM with a weighted combination of simple probabilistic functions constructs the likelihood functions by the probabilistic constraint in the similarity measures. In addition, our proposed method can online add a new client and update the information of registered clients due to the constructed APM. The experimental results eventually show the superior performance of our proposed system for both offline and real-time online testing.