We propose an adaptive background estimation algorithm for an outdoor video surveillance system. In order to enhance the ability of adaptation to illumination changes and variant noise in long-term running, an improve...
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ISBN:
(纸本)0780367251
We propose an adaptive background estimation algorithm for an outdoor video surveillance system. In order to enhance the ability of adaptation to illumination changes and variant noise in long-term running, an improved Kalman filtering model based on the local-region is discussed to dynamically estimate a background image, in which the parameters are predicted by a recursive-least-square adaptive filter. The experimental results on real-world video show that the algorithm can perform robustly and effectively.
Driver fatigue is one of the important factors that cause traffic accidents. The vision-based facial expression recognition technique is the most prospective method to detect driver fatigue. In this paper, we present ...
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Driver fatigue is one of the important factors that cause traffic accidents. The vision-based facial expression recognition technique is the most prospective method to detect driver fatigue. In this paper, we present a new driver fatigue detection based on unscented Kalman filter and eye tracking in this paper. The face is located using Haar algorithm firstly, which has good robustness in terms of head motions, variable lighting conditions, the change of hair and having glasses, etc. Secondly, the geometric properties and projection technique are used for eye location. Thirdly, we propose a new real time eye tracking method based on unscented Kalman Filter. Finally, driver fatigue can be detected whether the eyes are closed over 5 consecutive frames using vertical projection matching. The experimental results show validity of our method for driver fatigue detection under variable realistic conditions
Automatic analysis of digital video scenes often requires the segmentation of moving objects from the background. Historically, algorithms developed for this purpose have been restricted to small frame sizes, low fram...
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Automatic analysis of digital video scenes often requires the segmentation of moving objects from the background. Historically, algorithms developed for this purpose have been restricted to small frame sizes, low frame rates or offline processing. The simplest approach involves subtracting the current frame from the known background. However, as the background is unknown, the key is how to learn and model it. The paper proposes a new algorithm that represents each pixel in the frame by a group of clusters. The clusters are ordered according the likelihood that they model the background and are adapted to deal with background and lighting variations. Incoming pixels are matched against the corresponding cluster group and are classified according to whether the matching cluster is considered part of the background. The algorithm has been subjectively evaluated against three other techniques. It demonstrates equal or better segmentation than the other techniques and proves capable of processing 320/spl times/240 video at 28 fps, excluding post-processing.
This paper presents the PETS Metrics On-line Evaluation Service for computational visual surveillance algorithms. The service allows researchers to submit their algorithm results for evaluation against a set of applic...
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This paper presents the PETS Metrics On-line Evaluation Service for computational visual surveillance algorithms. The service allows researchers to submit their algorithm results for evaluation against a set of applicable metrics. The results of the evaluation processes are publicly displayed allowing researchers to instantly view how their algorithm performs against previously submitted algorithms. The approach has been validated using seven motion segmentation algorithms.
A new multiresolution SAR image formation algorithm called planar subarray processing, PSAP, is presented. The PSAP provides the capability to perform intermediate decisions using lower resolution image data to redire...
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A new multiresolution SAR image formation algorithm called planar subarray processing, PSAP, is presented. The PSAP provides the capability to perform intermediate decisions using lower resolution image data to redirect the remaining processing functions. These intermediate decisions can include, target detection, changedetection, super resolution, and moving target parameter estimation. The PSAP algorithm has a tree like flow structure using small cascaded FFT stages making it suitable for parallel processor implementation.
We propose a two stage motion-based region merging algorithm for content-based coding. It uses a six-parameter motion model and the change image obtained from the conventional change detector. First, the type of each ...
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We propose a two stage motion-based region merging algorithm for content-based coding. It uses a six-parameter motion model and the change image obtained from the conventional change detector. First, the type of each region is decided as one of an object, background and uncertain region using the change image. In the first region merging stage, all pairs of two adjacent object regions and two adjacent regions are extracted. Affine motion model parameters for each pair are computed. Then, the merging for one pair that has the smallest error is performed for each type. In particular a large threshold value is used to efficiently merge many redundant regions segmented in the background. In the second region merging stage, the merging for the adjacent uncertain regions or adjacent regions of different types are performed with the same procedure as that of the first stage. A small threshold value is used to merge the extracted adjacent regions which are represented with homogeneous motion parameters while preventing the merging of adjacent object region and background region in the wide range of the threshold value. From the experimental results the proposed merging algorithm is shown to efficiently reduce many redundant regions into one or a few regions in the wide range of the threshold value.
This paper considers the problem of multi-robot patrol around a closed area with the existence of an adversary attempting to penetrate into the area. In case the adversary knows the patrol scheme of the robots and the...
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This paper considers the problem of multi-robot patrol around a closed area with the existence of an adversary attempting to penetrate into the area. In case the adversary knows the patrol scheme of the robots and the robots use a deterministic patrol algorithm, then in many cases it is possible to penetrate with probability 1. Therefore this paper considers a non-deterministic patrol scheme for the robots, such that their movement is characterized by a probability p. This patrol scheme allows reducing the probability of penetration, even under an assumption of a strong opponent that knows the patrol scheme. We offer an optimal polynomial-time algorithm for finding the probability p such that the minimal probability of penetration detection throughout the perimeter is maximized. We describe three robotic motion models, defined by the movement characteristics of the robots. The algorithm described herein is suitable for all three models.
The proposed shot boundary determination (SBD) algorithm contains a set of finite state machine (FSM) based detectors for pure cut, fast dissolve, fade in, fade out, dissolve, and wipe. Support vector machines (SVM) a...
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The proposed shot boundary determination (SBD) algorithm contains a set of finite state machine (FSM) based detectors for pure cut, fast dissolve, fade in, fade out, dissolve, and wipe. Support vector machines (SVM) are applied to the cut and dissolve detectors to further boost performance. Our SBD system was highly effective when evaluated in TRECVID 2006 (TREC video retrieval evaluation) and its performance was ranked highest overall.
The problem of tracking the distance between two convex polyhedra is finding applications in many areas of robotics, including intersection detection, collision detection, and path planning. An algorithm for computing...
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The problem of tracking the distance between two convex polyhedra is finding applications in many areas of robotics, including intersection detection, collision detection, and path planning. An algorithm for computing the minimum distance between two convex polyhedra is presented. The algorithm applies to polyhedral objects that can be represented as convex hulls of its vertex value in three-dimensional space. Nonlinear programming techniques are then employed to compute the minimum distance between two convex polyhedra, and according to the minimum distance, it can conclude whether or not the objects has collided at some time. The simulation results demonstrate the performance of this new method
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