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VPEDA: A Robust Vantage Point Based Event Detection Architecture forEfficient Object Detection and Localization

机译:VPEDA:一种基于Vantage点的鲁棒事件检测架构,用于有效的对象检测和定位

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Currently the identification and localization of mobile objects have come as one of the most important approaches for event detection on road. VANET applications works on the principle of local gradient method that evaluates the fitness between projection of the vehicle model and object data. However, the mobile objects are highly susceptible to the appearance of the vehicle and displacement that changes at a frequent time period. In VANET, each mobile object broadcasts its unique id, the appearance and displacement in beacon packets. The appearance and displacement are the two important factors to be considered during event detection. For that purpose, the traffic scenarios in road network have to be monitored at different perspectives to notice the location information of mobile objects. This study put forwards a Vantage Point-based Event Driven Architecture (VPEDA) as a novel method to get insight into different levels of events during different time periods. Furthermore, Object identification and position forecast using local gradient method considers only the exterior of the vehicle and displacement in the road network and does not discuss about the occurrences of event at the vehicle junction points. The proposed VPEDA focuses on the event detection using the different neighbourhood information of the mobile objects during diversified time interval using a vantage point. VPEDA process the occurrences of event like accident met at traffic junctions during different interval of time. The located event at a particular vantage point identifies the neighbour mobile object that is taken as the nodal point for standardization of the traffic modality. Moreover, with the nodal point, the locator ensures that the reason behind the event to be identified in an early stage. An investigational assessment is carried out to analyse the impact of the vantage point on average vehicle speed, percentage of packets delivered and. Experimental results shows that VPEDA can detect the event which involve the different number of mobile objects with minimum delay.
机译:当前,移动物体的识别和定位已成为道路上事件检测的最重要方法之一。 VANET应用程序根据局部梯度法原理进行工作,该方法可评估车辆模型的投影与目标数据之间的适合度。但是,可移动物体极易受到车辆外观和在频繁的时间段内变化的位移的影响。在VANET中,每个移动对象都在信标数据包中广播其唯一ID,外观和位移。外观和位移是事件检测期间要考虑的两个重要因素。为此,必须从不同的角度监视道路网络中的交通场景,以注意移动对象的位置信息。这项研究提出了一种基于Vantage Point的事件驱动架构(VPEDA),作为一种新颖的方法来洞察不同时间段内不同级别的事件。此外,使用局部梯度法的目标识别和位置预测仅考虑车辆的外观和路网中的位移,而没有讨论在车辆交汇点的事件的发生。提出的VPEDA专注于在利用有利点的多样化时间间隔内使用移动对象的不同邻域信息进行事件检测。 VPEDA处理不同时间间隔内在交通枢纽遇到的事件(如事故)的发生。在特定有利位置的定位事件标识了邻居移动对象,该邻居移动对象被用作交通方式标准化的节点。此外,借助节点,定位器确保可以尽早识别出事件背后的原因。进行了一项研究评估,以分析有利位置对平均车速,已送出包裹的百分比等的影响。实验结果表明,VPEDA能够以最小的延迟检测到涉及不同数量的移动对象的事件。

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