A NOVEL METHOD FOR VIDEO SURVIELLANCE APPLICATION USING BACKGROUND SUBTRACTION
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RAHUL MATHAMSETTY, T.ADITYA KUMAR
Abstract - Vehicle detection and classification plays a prominent role in traffic surveillance systems. In the past many methods had been derived and implemented still we find some problems of detection and classification. These problems mainly occur in dynamic texture such as rain fall, moving trees, mountain and camera configuration. In order to overcome these problems on the vehicles they are some essential traffic parameters, such as vehicle counting, detection and classification. In this paper, to avoid detection and classification problems frame background subtraction has been proposed. Firstly the background method is used to detect the moving objects from the vehicle video. The morphological operation are applied to remove the noise regions and obtaining more accurate segmentation results. After vehicle detection, a object-based vehicle tracking method is used for building the correspondence between vehicles detected at different time instants. After vehicle tracking, we calculate the vehicle count from video. Experimental results are shown better segmentation and tracking results compare to Gaussian mixture of model (GMM).
RAHUL MATHAMSETTY, T.ADITYA KUMAR
Abstract - Vehicle detection and classification plays a prominent role in traffic surveillance systems. In the past many methods had been derived and implemented still we find some problems of detection and classification. These problems mainly occur in dynamic texture such as rain fall, moving trees, mountain and camera configuration. In order to overcome these problems on the vehicles they are some essential traffic parameters, such as vehicle counting, detection and classification. In this paper, to avoid detection and classification problems frame background subtraction has been proposed. Firstly the background method is used to detect the moving objects from the vehicle video. The morphological operation are applied to remove the noise regions and obtaining more accurate segmentation results. After vehicle detection, a object-based vehicle tracking method is used for building the correspondence between vehicles detected at different time instants. After vehicle tracking, we calculate the vehicle count from video. Experimental results are shown better segmentation and tracking results compare to Gaussian mixture of model (GMM).
ENHANCED RELIABLE DATA DELIVERY IN MOBILE ADHOC NETWORKS USING OPPORTUNISTIC PETAL ROUTING PROTOCOL
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S.Joy Kumar, Dr.A.Yesu Babu
ABSTRACT: This paper addresses the problem of delivering data packets for highly dynamic mobile ad hoc networks in a reliable and timely manner. Most existing ad hoc routing protocols are susceptible to node mobility, especially for large-scale networks. Driven by this issue, we propose an efficient Position-based Opportunistic Petal Routing (OPR) protocol which takes advantage of the stateless property of geographic routing and the broadcast nature of wireless medium. In the case of communication hole, a Virtual Destination-based Void Handling (VDVH) scheme is further proposed to work together with OPR. Both theoretical analysis and simulation results show that OPR achieves excellent performance even under high node mobility with acceptable overhead and the new void handling scheme also works well.
S.Joy Kumar, Dr.A.Yesu Babu
ABSTRACT: This paper addresses the problem of delivering data packets for highly dynamic mobile ad hoc networks in a reliable and timely manner. Most existing ad hoc routing protocols are susceptible to node mobility, especially for large-scale networks. Driven by this issue, we propose an efficient Position-based Opportunistic Petal Routing (OPR) protocol which takes advantage of the stateless property of geographic routing and the broadcast nature of wireless medium. In the case of communication hole, a Virtual Destination-based Void Handling (VDVH) scheme is further proposed to work together with OPR. Both theoretical analysis and simulation results show that OPR achieves excellent performance even under high node mobility with acceptable overhead and the new void handling scheme also works well.
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