Our research is focused on the development of novel machine vision based telematic systems, which provide non-intrusive probing of the state of the driver and driving conditions. In this paper we present a system whic...
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Our research is focused on the development of novel machine vision based telematic systems, which provide non-intrusive probing of the state of the driver and driving conditions. In this paper we present a system which allows simultaneous capture of the driver's head pose, driving view, and surroundings of the vehicle. The integrated machine vision system utilizes a video stream of full 360 degree panoramic field of view. The processing modules include perspective transformation, feature extraction, head detection, head pose estimation, driving view synthesis, and motion segmentation. The paper presents a multi-state statistical decision models with Kalman filtering based tracking for head pose detection and face orientation estimation. The basic feasibility and robustness of the approach is demonstrated with a series of systematic experimental studies.
作者：
W. Abd-AlmageedC.E. SmithS. RamadanRobotics
Artificial Intelligence and Vision Laboratory Department of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA
In this paper, a new non-parametric generalized formulation to statistical pressure snakes is presented. We discuss the shortcomings of the traditional pressure snakes. We then introduce a new generic pressure model t...
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In this paper, a new non-parametric generalized formulation to statistical pressure snakes is presented. We discuss the shortcomings of the traditional pressure snakes. We then introduce a new generic pressure model that alleviates these shortcomings, based on the Bayesian decision theory. Non-parametric techniques are used to obtain the statistical models that drive the snake. We discuss the advantages of using the proposed non-parametric model compared to other parametric techniques. Multi-colored-target tracking is used to demonstrate the performance of the proposed approach. Experimental results show enhanced, real-time performance.
Advances in intelligent transportation are rapidly sensing and connecting both highways and vehicles. This recent availability of high-speed communication and large quantities of information are rapidly changing the w...
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In recent years, large number of cameras have been installed in freeway and road environments. While the use of some of these cameras is being automated through computervision, few computervision systems allow for t...
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The Distributed Interactive Video Array (DIVA) system is developed to provide a large-scale, redundant cluster of video streams to observe a remote scene and to supply automatic focus of-attentian with event-driven se...
作者：
Abd-Almageed, WaelSmith, ChristopherRobotics
Artificial Intelligence and Vision Laboratory Electrical and Computer Engineering Department University of New Mexico United States
In this paper, a new technique for object classification from silhouettes is presented. Hidden Markov Models are used as a classification mechanism. Through a set of experiments, we show the validity of our approach a...
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ISBN:
(纸本)1889335185
In this paper, a new technique for object classification from silhouettes is presented. Hidden Markov Models are used as a classification mechanism. Through a set of experiments, we show the validity of our approach and show its invariance under severe rotation conditions. Also, a comparison with other techniques that use Hidden Markov Models for object classification from silhouettes is presented.
作者：
Abd-Almageed, WaelSmith, Christopher E.Robotics
Artificial Intelligence and Vision Laboratory Electrical and Computer Engineering Department University of New Mexico Albuquerque NM 87131
This paper introduces a new approach to statistical pressure snakes. It uses statistical modeling for both object and background to obtain a more robust pressure model. The Expectation Maximization (EM) algorithm is u...
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This paper introduces a new approach to statistical pressure snakes. It uses statistical modeling for both object and background to obtain a more robust pressure model. The Expectation Maximization (EM) algorithm is u...
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In recent years, large number of cameras have been installed in freeway and road environments. While the use of some of these cameras is being automated through computervision, few computervision systems allow for t...
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In recent years, large number of cameras have been installed in freeway and road environments. While the use of some of these cameras is being automated through computervision, few computervision systems allow for true wide-area large scaled automation. This paper describes a distributed computing system capable of managing an arbitrarily large sensor network using only common computing and networking platforms. The architecture is capable of handling many common computervision tasks, as well as the inter-sensor communication necessary for developing new algorithms which employ data from multiple sensors. This system is tested with an algorithm which tracks moving objects through a prototype camera network with non-overlapping fields or view in a college campus environment This algorithm allows the system to maintain the identity of a tracked objects as it leaves and enters the fields of view of individual sensors. Such an algorithm is necessary for applications which require tracking objects over large distances or over long periods of time in an environment without complete sensor coverage.
We present face recognition schemes based on video streams: the majority decision rule and HMM maximum likelihood (ML) decision rules. PCA type of subspace feature analysis is first applied to the face images in a vid...
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We present face recognition schemes based on video streams: the majority decision rule and HMM maximum likelihood (ML) decision rules. PCA type of subspace feature analysis is first applied to the face images in a video segment of a fixed number of frames. The majority decision rule is then applied to PCA recognition results in the video segment. Discrete HMM (DHMM) is also applied to the single-frame recognition sequences. Continuous density HMM (CDHMM) is applied directly to the sequence of PCA feature vectors for ML decision on the video segment in a delayed decision manner. Experimental results are compared between these three schemes in terms of the number of states and Gaussian mixtures of the HMMs. CDHMM-based decision rule achieved a 99% correct recognition rate in average. A geometric interpretation of ML in the feature subspace well explains the observed performances.
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