混合摄像机网络中的事件预测

U. M. Erdem, S. Sclaroff
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引用次数: 9

摘要

给定一个混合摄像头布局——例如,一个包含静态和活动摄像头——人们按照既定的交通模式四处走动,我们的目标是预测摄像头的子集、各自的摄像头参数设置和未来的时间窗口,这些时间窗口最有可能导致视觉任务的成功,例如,当摄像头观察到感兴趣的事件时,人脸识别。我们提出了一个自适应概率模型,随着时间的推移,随着摄像机报告观察到的事件,摄像机的时间相关性随之增加。不需要相机的外部、内部或颜色校准。我们使用改进的顺序蒙特卡罗方法有效地获得了摄像机参数预测。我们在模拟和真实环境实验中使用几个有源摄像机演示了该模型在人脸检测场景中的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Event prediction in a hybrid camera network
Given a hybrid camera layout—one containing, for example, static and active cameras—and people moving around following established traffic patterns, our goal is to predict a subset of cameras, respective camera parameter settings, and future time windows that will most likely lead to success the vision tasks, such as, face recognition when a camera observes an event of interest. We propose an adaptive probabilistic model that accrues temporal camera correlations over time as the cameras report observed events. No extrinsic, intrinsic, or color calibration of cameras is required. We efficiently obtain the camera parameter predictions using a modified Sequential Monte Carlo method. We demonstrate the performance of the model in an example face detection scenario in both simulated and real environment experiments, using several active cameras.
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