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首页> 外文会议>Conference on Multimedia Computing and Networking; 20080130-31; San Jose,CA(US) >Exploiting Semantics for Sensor Re-Calibration in Event Detection Systems
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Exploiting Semantics for Sensor Re-Calibration in Event Detection Systems

机译：利用语义技术对事件检测系统中的传感器进行重新校准

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摘要

Event detection from a video stream is becoming an important and challenging task in surveillance and sentient systems. While computer vision has been extensively studied to solve different kinds of detection problems over time, it is still a hard problem and even in a controlled environment only simple events can be detected with a high degree of accuracy. Instead of struggling to improve event detection using image processing only, we bring in semantics to direct traditional image processing. Semantics are the underlying facts that hide beneath video frames, which can not be "seen" directly by image processing. In this work we demonstrate that time sequence semantics can be exploited to guide unsupervised re-calibration of the event detection system. We present an instantiation of our ideas by using an appliance as an example - Coffee Pot level detection based on video data - to show that semantics can guide the re-calibration of the detection model. This work exploits time sequence semantics to detect when re-calibration is required to automatically re-learn a new detection model for the newly-evolved system state and to resume monitoring with a higher rate of accuracy.
机译：在监视和感知系统中，从视频流进行事件检测已成为一项重要且具有挑战性的任务。尽管已经对计算机视觉进行了广泛的研究以解决各种检测问题，但仍是一个难题，即使在受控环境中，也只能以高度准确的方式检测到简单事件。与其努力仅使用图像处理来改善事件检测，不如引入语义来指导传统图像处理。语义是隐藏在视频帧下方的基本事实，无法通过图像处理直接“看到”。在这项工作中，我们证明了可以利用时序语义来指导事件检测系统的无监督重新校准。我们以一个设备为例-基于视频数据的咖啡壶液位检测-展示了我们的想法的实例化，以表明语义可以指导检测模型的重新校准。这项工作利用时间序列语义来检测何时需要重新校准，以便为重新发展的系统状态自动重新学习新的检测模型，并以更高的准确率恢复监视。

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