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首页> 外文会议>Automatic target recognition XXIII >Hot Spot Detection and Classification in LWIR Videos for Person Recognition
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Hot Spot Detection and Classification in LWIR Videos for Person Recognition

机译：LWIR视频中的热点检测和分类以识别人

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摘要

Person recognition is a key issue in visual surveillance. It is needed in many security applications such as intruder detection in military camps but also for gaining situational awareness in a variety of different safety applications. A solution for LWIR videos coming from a moving camera is presented that is based on hot spot classification to distinguish persons from background clutter and other objects. We especially consider objects in higher distance with small appearance in the image. Hot spots are detected and tracked along the videos. Various image features are extracted from the spots and different classifiers such as SVM or AdaBoost are evaluated and extended to utilize the temporal information. We demonstrate that taking advantage of this temporal context can improve the classification performance.
机译：人的识别是视觉监控中的关键问题。它在许多安全应用程序中都是需要的，例如在军事营地中进行入侵者检测，但在各种不同的安全应用程序中也需要获得态势感知。提出了一种针对来自移动摄像机的LWIR视频的解决方案，该解决方案基于热点分类来区分背景杂物和其他物体中的人物。我们特别考虑距离较远且图像外观较小的对象。沿视频检测并跟踪热点。从斑点中提取各种图像特征，并评估和扩展不同的分类器（例如SVM或AdaBoost）以利用时间信息。我们证明利用这种时间上下文可以提高分类性能。

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