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This paper presents a novel approach for eliminating unexpected shadows from multiple pedestrians from a static and textured background using Gaussian shadow modeling. First, a set of moving regions are segmented from the static background using a background subtraction technique. The extracted moving region may contain multiple shadows from various(More)
This paper presents an automatic traffic surveillance system to estimate important traffic parameters from video sequences using only one camera. Different from traditional methods that can classify vehicles to only cars and noncars, the proposed method has a good ability to categorize vehicles into more specific classes by introducing a new "linearity"(More)
This paper presents a coarse-to-fine approach to eliminate unexpected shadows of multiple pedestrians from a static and textured background using Gaussian shadow modeling. At the coarse stage, a moment-based method is proposed to estimate the rough boundaries between shadows and moving objects. Then, at the fine stage, the rough approximation of shadow(More)
This paper presents an automatic traffic surveillance system to estimate important traffic parameters from video sequences using only one camera. Different from traditional methods which classify vehicles into only cars and non-cars, the proposed method has a good capability to categorize cars into more specific classes with a new “linearity” feature. In(More)
This paper proposes a novel shadow elimination method for solving the shadow occlusion problems of vehicle analysis. Different from traditional methods which only consider intensity properties in shadow modeling, this method introduces a new important feature to eliminate all unwanted shadows, i.e., lane line geometries. In this approach, a set of moving(More)
This paper presents an automatic traffic surveillance system for tracking and classification vehicles in traffic video sequences. In order to detect moving objects from a dynamic background scene, which may have temporal clutters, we devised an adaptive background update method and a motion classification rule. A two-dimensional tokenbased tracking system(More)
This paper develops an intelligent robust control algorithm for a class of uncertain nonlinear multivariable systems by using sliding model technology. The proposed control algorithm consists of an adaptive recurrent cerebellar model articulation controller (RCMAC) and a robust controller. The adaptive RCMAC is a main tracking controller utilized to mimic(More)
This paper presents a vehicle occlusion identification system based on the perceptive characters of a roadway. To tracking the vehicles, two stages are devised, initial parameter setting stage and occlusion handling stage. In the initial stage, a background extracting method is adopted to obtain the first clean background. Then, a road detection algorithm(More)
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