A system aiming at detecting driver drowsiness or fatigue on the basis of video analysis is presented. The focus of this paper is on how to extract driver yawning. A real time face detector is implemented to locate dr...
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ISBN:
(纸本)0780390059
A system aiming at detecting driver drowsiness or fatigue on the basis of video analysis is presented. The focus of this paper is on how to extract driver yawning. A real time face detector is implemented to locate driver's face region. Subsequently, Kalman filter is adapted to track face region. Further, mouth window is localized within face region and degree of mouth openness is extracted based on mouth features to determine driver yawning in video. The system will reinitialize when occlusion or miss-detect on happen. Experiments are conducted to evaluate the validity of the described method.
This paper investigated the performances of a well-known car-following model with numerical simulations in describing the deceleration process induced by the motion of a leading car. A leading car with a pre-specified...
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This paper investigated the performances of a well-known car-following model with numerical simulations in describing the deceleration process induced by the motion of a leading car. A leading car with a pre-specified speed profile was used to test the above model. The results show that this model is to some extent deficient in performing the process aforementioned. Modifications of the model to overcome these deficiencies were demonstrated and a modified car-following model was proposed accordingly. Furthermore, the delay time of car motion of the new model were studied.
We present an algebraic approach to multibody motion segmentation from line correspondences. Given three perspective views containing multiple linearly moving objects, we demonstrate that after applying a polynomial e...
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ISBN:
(纸本)0769523722
We present an algebraic approach to multibody motion segmentation from line correspondences. Given three perspective views containing multiple linearly moving objects, we demonstrate that after applying a polynomial embedding to the line correspondences, they became related by the so-called multibody line constraint of translational motions. We show how to linearly estimate the multibody trifocal epipole from line-line-line correspondences. The individual trifocal epipoles are then obtained from the derivatives of the multibody line constraint (up to an unknown factor). Given normalized trifocal epipoles, we can use any special clustering technique to obtain the clustering of the motions and the correspondences. The limitations of the proposed algorithm are also discussed. Experimental results on synthetic and real dynamic scenes are presented.
A new star acquisition algorithm i.e., extremal point approach is proposed. The method, by making use of Least Square Support Vector Machine (LSSVM) well fits the star image intensity surface over the neighborhood, an...
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A new star acquisition algorithm i.e., extremal point approach is proposed. The method, by making use of Least Square Support Vector Machine (LSSVM) well fits the star image intensity surface over the neighborhood, and determines the maximal extremal points on the fitted surface, and then obtains possible star centers. Star cluster grouping from the candidate star centers speeds up the procedure of star acquisition. For simulated star image where the exact location of stars is known, the average sum of invert square distance between a declared star center and the nearest ideal star center, as the merit figure of star acquisition algorithms, is calculated under different noisy conditions, thus optimizing LSSVM parameters. To maximize the merit figure, when the kernel size is 5 × 5 pixels, the optimal configuration of parameters (σ2,γ) for the LSSVM with Gaussian kernel is (17, 1.25). The proposed algorithm has similar efficiency to vector method while providing higher merit figure.
In this paper, we introduce methods to extract low-level features for soccer video analysis. A new method is proposed to segment players by using a mean distributed color feature. In order to discriminate which team t...
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ISBN:
(纸本)0780390059
In this paper, we introduce methods to extract low-level features for soccer video analysis. A new method is proposed to segment players by using a mean distributed color feature. In order to discriminate which team the player belongs to, we use mutual chromatic correlation degree of players to identify team without extracting templates of players in advance. Experimental results are included to show the effectiveness of the method.
Target detection and location in infrared clutter background is very important to infrared search and track system. Especially for small target detection in infrared image in background of sea and sky, there are no ge...
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A method used for recognition and understanding of airfield based on mathematical morphology is proposed in this paper. The new approach can he divided into three steps. First, to extract the typical geometric structu...
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A method used for recognition and understanding of airfield based on mathematical morphology is proposed in this paper. The new approach can he divided into three steps. First, to extract the typical geometric structure features of airfield, a segmentation method called recursive Otsu algorithm is employed on an airfield image. Second, thinning and shrinking algorithms are utilized to obtain the contour of airfield with single pixel and to remove diffused small particles. Finally, Radon transform is adopted to extract two typical and important components, primary and secondary runways of airfield exactly. At the same time, region growing algorithm is exploited to get the other components such as parking apron and garages. The experimental results demonstrate that the proposed method gives good performance.
Efficient VLSI architectures for multi-dimensional (m-D) discrete wavelet transform (DWT), e.g. m=2, 3, are presented, in which the lifting scheme of DWT is used to reduce efficiently hardware complexity. The parallel...
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Efficient VLSI architectures for multi-dimensional (m-D) discrete wavelet transform (DWT), e.g. m=2, 3, are presented, in which the lifting scheme of DWT is used to reduce efficiently hardware complexity. The parallelism of 2 m subbands transforms in lifting-based m-D DWT is explored, which increases efficiently the throughput rate of separable m-D DWT. The proposed architecture is composed of m2m-1 1-D DWT modules working in parallel and pipelined, which is designed to process 2m input samples per clock cycle, and generate 2m subbands coefficients synchronously. The total time of computing one level of decomposition for a 2-D image (3-D image sequence) of size N2 (MN2) is approximately N2/4 (MN2/8) intra- clock cycles (ccs). An efficient line-based architecture framework for both 2D+t and t+2D 3-D DWT is first proposed. Compared with the similar works reported in previous literature, the proposed architecture has good performance in terms of production of computation time and hardware cost. The proposed architecture is simple, regular, scalable and well suited for VLSI implementation.
The aim of modulation classification (MC) is to identify the modulation type of a communication signal. It plays an important role in many cooperative or noncooperative communication applications. Three spectrogram-ba...
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The aim of modulation classification (MC) is to identify the modulation type of a communication signal. It plays an important role in many cooperative or noncooperative communication applications. Three spectrogram-based modulation classification methods are proposed. Their reccgnition scope and performance are investigated or evaluated by theoretical analysis and extensive simulation studies. The method taking moment-like features is robust to frequency offset while the other two, which make use of principal component analysis (PCA) with different transformation inputs,can achieve satisfactory accuracy even at low SNR (as low as 2 dB). Due to the properties of spectrogram, the statistical patternrecognition techniques, and the image preprocessing steps, all of our methods are insensitive to unknown phase and frequency offsets, timing errors, and the arriving sequence of symbols.
An improved MBNN (model-based neural network) was proposed to segment images. An image model obtained by the Markov random filed (MRF) was introduced into the MBNN. The MRF's parameters were estimated by modified ...
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An improved MBNN (model-based neural network) was proposed to segment images. An image model obtained by the Markov random filed (MRF) was introduced into the MBNN. The MRF's parameters were estimated by modified expectation-maximization (EM) algorithm. The technique of pre-assigning a class number was employed to decrease the computation burden. Therefore the task of image segmentation was implemented by the network. The experiment results show that it is feasible to apply the improved MBNN to image segmentation since a priori knowledge is excellently combined with local statistical correlation.
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