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首页> 外文会议>2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics >Keyframe extraction using AdaBoost
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Keyframe extraction using AdaBoost

机译：使用AdaBoost提取关键帧

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摘要

An approach for keyframe extraction using AdaBoost is proposed which is based on foreground detection. The aim of this approach is to extract keyframes from sequences of specific vehicle images of lane vehicle surveillance video. This method utilizes integral channel features and the area feature as the image feature descriptor, combined with training an AdaBoost classifier. The experimental results on real-road test video show that the algorithm presented in this paper effectively selects the most distinct and clearest image for a sequence of vehicle images which begins counting when a motional vehicle enters into the surveillance area and ends when it leaves. Compared with other methods, it has increased the effectiveness and precision for keyframe extraction of lane vehicle surveillance video and achieves more effective compression of video analytical data for lane vehicle surveillance.
机译：提出了一种基于前景检测的基于AdaBoost的关键帧提取方法。这种方法的目的是从车道监控视频的特定车辆图像序列中提取关键帧。该方法利用积分通道特征和区域特征作为图像特征描述符，并结合训练AdaBoost分类器。实际路测视频的实验结果表明，本文提出的算法有效地为一系列车辆图像选择了最清晰，最清晰的图像，当运动车辆进入监视区域时，该图像开始计数，而当车辆离开监视区域时，该算法结束。与其他方法相比，它提高了车道监控视频关键帧提取的效率和精度，并实现了对车道监控视频分析数据更有效的压缩。

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