University of California, Riverside, CA 92521 USA.
IEEE Trans Image Process. 2010 Oct;19(10):2564-79. doi: 10.1109/TIP.2010.2052823. Epub 2010 Jun 14.
Camera networks are being deployed for various applications like security and surveillance, disaster response and environmental modeling. However, there is little automated processing of the data. Moreover, most methods for multicamera analysis are centralized schemes that require the data to be present at a central server. In many applications, this is prohibitively expensive, both technically and economically. In this paper, we investigate distributed scene analysis algorithms by leveraging upon concepts of consensus that have been studied in the context of multiagent systems, but have had little applications in video analysis. Each camera estimates certain parameters based upon its own sensed data which is then shared locally with the neighboring cameras in an iterative fashion, and a final estimate is arrived at in the network using consensus algorithms. We specifically focus on two basic problems-tracking and activity recognition. For multitarget tracking in a distributed camera network, we show how the Kalman-Consensus algorithm can be adapted to take into account the directional nature of video sensors and the network topology. For the activity recognition problem, we derive a probabilistic consensus scheme that combines the similarity scores of neighboring cameras to come up with a probability for each action at the network level. Thorough experimental results are shown on real data along with a quantitative analysis.
摄像机网络正在被部署用于各种应用，如安全和监控、灾害响应和环境建模。然而，对数据的自动化处理很少。此外，大多数多摄像机分析方法都是集中式方案，需要将数据集中在中央服务器上。在许多应用中，从技术和经济角度来看，这都是非常昂贵的。在本文中，我们通过利用多智能体系统中研究的一致性概念来研究分布式场景分析算法，但这些概念在视频分析中应用甚少。每台摄像机根据其自身感知的数据来估计某些参数，然后以迭代的方式在本地与相邻摄像机共享，最后使用一致性算法在网络中得出最终估计。我们特别关注两个基本问题——跟踪和活动识别。对于分布式摄像机网络中的多目标跟踪，我们展示了如何适应卡尔曼一致性算法，以考虑视频传感器的方向性和网络拓扑。对于活动识别问题，我们推导出一种概率一致性方案，该方案结合了相邻摄像机的相似性得分，在网络级别上为每个动作生成概率。我们在真实数据上进行了彻底的实验，并进行了定量分析。
