以HOG、LUV和光流为特征，以AdaBoost为分类器进行行人检测

2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2016-12-01 DOI:10.1109/IPTA.2016.7821024

Rabia Rauf, A. R. Shahid, Sheikh Ziauddin, A. Safi

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引用次数: 11

摘要

行人检测已被用于汽车安全、视频监控和智能车辆等应用。本文提出了一种基于HOG、LUV和光流特征和AdaBoost Decision Stump分类器的行人检测方案。我们在Caltech-USA行人数据集上的实验表明，该方法取得了令人满意的结果，对数平均缺失率约为16.7%。

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Pedestrian detection using HOG, LUV and optical flow as features with AdaBoost as classifier

Pedestrian detection has been used in applications such as car safety, video surveillance, and intelligent vehicles. In this paper, we present a pedestrian detection scheme using HOG, LUV and optical flow features with AdaBoost Decision Stump classifier. Our experiments on Caltech-USA pedestrian dataset show that the proposed scheme achieves promising results of about 16.7% log-average miss rate.

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2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)

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