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首页> 外文期刊>International Journal of Innovative Computing Information and Control >REAL-TIME BOOSTED VEHICLE DETECTION DEAL WITH HIGH DETECTION RATE USING FALSE ALARM ELIMINATING METHOD
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REAL-TIME BOOSTED VEHICLE DETECTION DEAL WITH HIGH DETECTION RATE USING FALSE ALARM ELIMINATING METHOD

机译：虚假报警消除方法实时检测高检出率车辆

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摘要

We present in this paper a high detection rate of Boosted vehicle detection. The positive database of Boosted training usually consists of similar contour or lighting of vehicle image, but in our work the training data is extracted from both daytime and evening images, it means different in lighting, and we are forced to stop Boosted training toward a high detection rate, which results in a relatively high false alarm rate. Therefore, the Boosted detector will detect vehicle candidate as much as possible. Moreover, we develop two false alarm eliminating methods to eliminate the false vehicle candidates. The algorithm consists of edge complexity for daytime case, and combination histogram matching and intensity complexity for evening case. Each case is chosen by automatic switcher algorithm. To provide real-time detection, we also proposed position based sliding window detector. The idea is based on sliding windows size selection relative to the position of vehicle candidate in an image. Thus, we do not need to apply various size of detector size as traditional Boosted algorithm. Finally, our experimental results show that our proposed system can operate in a real-world environment, while providing realtime detection.
机译：我们在本文中提出了高效率的助推车辆检测率。 Boosted训练的正面数据库通常由相似的轮廓或车辆图像照明组成，但在我们的工作中，训练数据是从白天和晚上的图像中提取的，这意味着照明有所不同，因此我们不得不将Boosted训练停止向较高的方向发展。检测率，导致较高的误报率。因此，Boosted检测器将尽可能多地检测车辆候选者。此外，我们开发了两种消除虚假警报的方法来消除虚假的候选车辆。该算法包括白天情况下的边缘复杂度，晚上情况下的组合直方图匹配和强度复杂度。每种情况均由自动切换器算法选择。为了提供实时检测，我们还提出了基于位置的滑动窗口检测器。这个想法是基于相对于图像中候选车辆位置的滑动窗口大小选择。因此，我们不需要像传统的Boosted算法那样应用各种大小的检测器。最后，我们的实验结果表明，我们提出的系统可以在现实环境中运行，同时提供实时检测。

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