People detection based on co-occurrence of appearance and spatiotemporal features
People detection based on co-occurrence of appearance and spatiotemporal features
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DOI：
10.1109/icpr.2008.4761809
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发表时间：
2008-12
期刊：
影响因子：
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通讯作者：
Yuji Yamauchi;H. Fujiyoshi;Bon-Woo Hwang;T. Kanade
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文献类型：
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作者：
Yuji Yamauchi;H. Fujiyoshi;Bon-Woo Hwang;T. Kanade
提出了一种基于外观特征和时空特征共现的人体检测方法。方向梯度直方图（HOG）被用作外观特征，像素状态分析的结果被用作时空特征。像素状态分析将前景像素分类为静止或瞬态。通过主成分分析（PCA）将外观和时空特征投影到子空间中，以降低向量的维数。级联AdaBoost分类器用于表示外观和时空特征的共现。特征同现的使用，捕捉相似的外观，运动和空间信息的人类内，使它成为一个有效的检测器。实验结果表明，我们的方法的性能是约29%，比传统的方法。
This paper presents a method for detecting people based on the co-occurrence of appearance and spatiotemporal features. Histograms of oriented gradients(HOG) are used as appearance features, and the results of pixel state analysis are used as spatiotemporal features. The pixel state analysis classifies foreground pixels as either stationary or transient. The appearance and spatiotemporal features are projected into subspaces in order to reduce the dimensions of the vectors by principal component analysis(PCA). The cascade AdaBoost classifier is used to represent the co-occurrence of the appearance and spatiotemporal features. The use of feature co-occurrence, which captures the similarity of appearance, motion, and spatial information within the people class, makes it an effective detector. Experimental results show that the performance of our method is about 29% better than that of the conventional method.