siddhartha meshram - Academia.edu (original) (raw)
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Papers by siddhartha meshram
The difficulty of obtaining the initial background there is the inaccuracy of real-time backgroun... more The difficulty of obtaining the initial background there is the inaccuracy of real-time background update and the difficulty of controlling the update speed in moving vehicle detection of traffic video this paper proposes an accurate and effective moving vehicle detection method which can be used in complex traffic environment. This method first constructs initial background image according to the real-time situation of traffic environment then segmentalizes the current frame into foreground region and background region accurately using the combined method of inter-frame difference and subtraction method. The experimental results show that this method can detect moving vehicles fast and accurately in complex traffic situation. Vehicle detection and tracking applications play an important role for civilian and military applications such as in highway traffic surveillance control management and urban traffic planning. Experimental results show that this method can detect moving vehicl...
It is important to know the road traffic density for effective traffic management and intelligent... more It is important to know the road traffic density for effective traffic management and intelligent transportation system (ITS). The increasing traffic volume creates a greatest challenge in today’s traffic research. This work is to detecting moving vehicles in video streams of traffic scenes recorded by low resolution cameras using some of the image processing techniques. Vision based traffic surveillance is a fast emerging field in road management schemes and highway monitoring. Video cameras are used to provide a rich information source for human understanding. Video sequences are captured and tested with the image processing techniques. Many methods and algorithms have been proposed in this paper to detect vehicles on highways..
IJARCCE, 2015
Background subtraction is a powerful mechanism for detecting change in a sequence of images that ... more Background subtraction is a powerful mechanism for detecting change in a sequence of images that finds many applications. The background subtraction methods apply probabilistic models to background intensities evolving in time nonparametric and mixture-of Gaussians. The main difficulty in designing a robust background subtraction algorithm is the selection of a detection threshold. In this we adapt threshold to varying video statistics by means of two statistical models. In addition to a nonparametric background model we introduce a foreground model based on small spatial neighborhood to improve discrimination sensitivity we also apply a Markov model to change labels to improve spatial coherence of the detections, the proposed methodology is applicable to other background models as well. The strength of the scheme lies in its simplicity and the fact that it defines an intensity range for each pixel location in the background to accommodate illumination variation as well as motion in the background. The efficacy of the scheme is shown through comparative analysis with competitive methods. Both visual as well as quantitative measures show an improved performance and the scheme has a strong potential for applications in real time surveillance.
The difficulty of obtaining the initial background there is the inaccuracy of real-time backgroun... more The difficulty of obtaining the initial background there is the inaccuracy of real-time background update and the difficulty of controlling the update speed in moving vehicle detection of traffic video this paper proposes an accurate and effective moving vehicle detection method which can be used in complex traffic environment. This method first constructs initial background image according to the real-time situation of traffic environment then segmentalizes the current frame into foreground region and background region accurately using the combined method of inter-frame difference and subtraction method. The experimental results show that this method can detect moving vehicles fast and accurately in complex traffic situation. Vehicle detection and tracking applications play an important role for civilian and military applications such as in highway traffic surveillance control management and urban traffic planning. Experimental results show that this method can detect moving vehicl...
It is important to know the road traffic density for effective traffic management and intelligent... more It is important to know the road traffic density for effective traffic management and intelligent transportation system (ITS). The increasing traffic volume creates a greatest challenge in today’s traffic research. This work is to detecting moving vehicles in video streams of traffic scenes recorded by low resolution cameras using some of the image processing techniques. Vision based traffic surveillance is a fast emerging field in road management schemes and highway monitoring. Video cameras are used to provide a rich information source for human understanding. Video sequences are captured and tested with the image processing techniques. Many methods and algorithms have been proposed in this paper to detect vehicles on highways..
IJARCCE, 2015
Background subtraction is a powerful mechanism for detecting change in a sequence of images that ... more Background subtraction is a powerful mechanism for detecting change in a sequence of images that finds many applications. The background subtraction methods apply probabilistic models to background intensities evolving in time nonparametric and mixture-of Gaussians. The main difficulty in designing a robust background subtraction algorithm is the selection of a detection threshold. In this we adapt threshold to varying video statistics by means of two statistical models. In addition to a nonparametric background model we introduce a foreground model based on small spatial neighborhood to improve discrimination sensitivity we also apply a Markov model to change labels to improve spatial coherence of the detections, the proposed methodology is applicable to other background models as well. The strength of the scheme lies in its simplicity and the fact that it defines an intensity range for each pixel location in the background to accommodate illumination variation as well as motion in the background. The efficacy of the scheme is shown through comparative analysis with competitive methods. Both visual as well as quantitative measures show an improved performance and the scheme has a strong potential for applications in real time surveillance.