Target detection and counting using a progressive certainty map in distributed visual sensor networks

M. Karakaya, H. Qi
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引用次数: 20

Abstract

Visual sensor networks (VSNs) merge computer vision, image processing and wireless sensor network disciplines to solve problems in multi-camera applications by providing valuable information through distributed sensing and collaborative in-network processing. Collaboration in sensor networks is necessary not only to compensate for the processing, sensing, energy, and bandwidth limitations of each sensor node but also to improve the accuracy and robustness of the sensor network. Collaborative processing in VSNs is more challenging than in conventional scalar sensor networks (SSNs) because of two unique features of cameras, including the extremely higher data rate compared to that of scalar sensors and the directional sensing characteristics with limited field of view. In this paper, we study a challenging computer vision problem, target detection and counting in VSN environment. Traditionally, the problem is solved by counting the number of intersections of the backprojected 2D cones of each target. However, the existence of visual occlusion among targets would generate many false alarms. In this work, instead of resolving the uncertainty about target existence at the intersections, we identify and study the non-occupied areas in the cone and generate the so-called certainty map of non-existence of targets. This way, after fusing inputs from a set of sensor nodes, the unresolved regions on the certainty map would be the location of target. This paper focuses on the design of a light-weight, energy-efficient, and robust solution where not only each camera node transmits a very limited amount of data but that a limited number of camera nodes is used. We propose a dynamic itinerary for certainty map integration where the entire map is progressively clarified from sensor to sensor. When the confidence of the certainty map is satisfied, a geometric counting algorithm is applied to find the estimated number of targets. In the conducted experiments using real data, the results of the proposed distributed and progressive method shows effectiveness in detection accuracy and energy and bandwidth efficiency.
分布式视觉传感器网络中基于渐进式确定性映射的目标检测与计数
视觉传感器网络(VSNs)融合了计算机视觉、图像处理和无线传感器网络等学科,通过分布式感知和协同网络处理提供有价值的信息,解决多相机应用中的问题。传感器网络中的协作不仅可以弥补每个传感器节点在处理、传感、能量和带宽方面的限制,还可以提高传感器网络的准确性和鲁棒性。与传统的标量传感器网络(ssn)相比,vsn中的协同处理更具挑战性,因为相机具有两个独特的特性,包括与标量传感器相比具有极高的数据速率和有限视场的方向传感特性。本文研究了一个具有挑战性的计算机视觉问题——VSN环境下的目标检测与计数。传统上,该问题是通过计算每个目标的反向投影二维锥体的相交次数来解决的。然而,目标间存在视觉遮挡会产生很多误报。在这项工作中,我们不是解决交叉口目标存在的不确定性,而是识别和研究锥体中未被占用的区域,并生成所谓的目标不存在的确定性图。这样,在融合一组传感器节点的输入后,确定性图上未解决的区域就是目标的位置。本文的重点是设计一个轻量级、节能和健壮的解决方案,不仅每个摄像机节点传输的数据量非常有限,而且使用的摄像机节点数量也有限。我们提出了一个动态行程的确定性地图集成,整个地图逐步澄清从传感器到传感器。当确定性映射的置信度满足时,采用几何计数算法求出目标的估计数量。在实际数据实验中,所提出的分布式渐进式方法在检测精度、能量和带宽效率方面都取得了良好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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