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Automated object detection and tracking based on clustered sensor networks.

机译：基于群集传感器网络的自动对象检测和跟踪。

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Automated visual surveillance is becoming an increasingly interesting topic for the scientific community because of changing security needs. The need for developing computer systems to provide enough information to take rapid and preventive action against security threats is great. Recent advances in VLSI design and wireless communication make possible the development of low power and small size sensor nodes that are capable of communicating with each other, without supervision from a base station, over short distances. Such developments pave the way for the design and realization of a visual surveillance system based on sensor networks.; An architecture based on a visual sensor network that performs automated object detection and tracking is presented. A processing unit attached to each camera detects objects moving in the video stream. Then the detected object is sent to a multi-agent tracking system. The information is sent to higher tier processing units for further processing. Object detection is by background subtraction.; Traditionally, background subtraction techniques have been implemented in software. However, software implementation is expensive in terms of power; therefore, it is not a good option for sensor network nodes. An efficient alternative for sensor nodes is to implement the Wronskian Change Detector in hardware as discussed in this work.; Visual information can be complemented by infrared images for concealed weapon detection. Visual information provides identity data while IR images supply localization of the concealed weapon. Image fusion incorporates the complementary information from two sources to improve detection and recognition. An image fusion scheme based on a wavelet transform is proposed.; In certain applications, it may be necessary to incorporate non-visual sensors, such as chemical sensors, in addition to visual sensors. Such non-visual sensors can be viewed as simpler nodes in the hierarchical proposed system of nodes. These sensors are organized in a two-tier network where resource and power-restricted motes form the lower tier. The upper tier is formed by stargate-class nodes designated as masters. A master selects four motes to be sentinels in order to detect an event of interest. The proposed data fusion scheme is a linear mean square estimator based on correlation coefficients.
机译：由于不断变化的安全需求，自动视觉监视已成为科学界越来越感兴趣的话题。迫切需要开发计算机系统以提供足够的信息，以针对安全威胁采取快速的预防措施。 VLSI设计和无线通信的最新进展使低功耗和小尺寸传感器节点的开发成为可能，这些节点能够在短距离内相互通信而无需基站的监管。这种发展为基于传感器网络的视觉监控系统的设计和实现铺平了道路。提出了一种基于视觉传感器网络的体系结构，该体系执行自动的对象检测和跟踪。安装在每个摄像机上的处理单元检测视频流中移动的对象。然后，将检测到的对象发送到多主体跟踪系统。该信息被发送到更高层的处理单元以进行进一步处理。通过背景减法来检测物体。传统上，背景扣除技术已在软件中实现。但是，软件实现在功能上是昂贵的。因此，对于传感器网络节点而言，这不是一个好的选择。传感器节点的一种有效替代方法是在本工作中讨论的在硬件中实现Wronskian变化检测器。可视信息可以通过红外图像进行补充，以进行隐藏武器检测。视觉信息提供身份数据，而红外图像提供隐藏武器的定位。图像融合结合了来自两个来源的补充信息，以改善检测和识别能力。提出了一种基于小波变换的图像融合方案。在某些应用中，除视觉传感器之外，可能还需要并入非视觉传感器，例如化学传感器。可以将这种非可视传感器视为分层建议的节点系统中的较简单节点。这些传感器被组织在两层网络中，其中资源和功率受限的微粒形成了较低层。上层由指定为主机的星际之门级节点组成。主人选择四个要点以检测事件。所提出的数据融合方案是基于相关系数的线性均方估计器。

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