通过k折采样与逐步后向剔除，提升人体检测速度与准确率
k-fold Subsampling based Sequential Backward Feature Elimination
- 结合k折采样与序列后向特征剔除，优化特征选择流程
- 检测速度提升超50%，准确率最高提升2%，优于主流算法
- 适合需要高效人体检测的实时视觉系统应用
我们提出一种新型包装式特征选择算法，用于人体检测。该算法融合滤波法与包装法的优点，能够选出最能表征图像中目标形状的最优特征子集。具体而言，所提算法采用k折采样与序列后向剔除策略，以标准线性支持向量机（SVM）作为人体检测分类器。在公开的INRIA和ETH行人全图数据集上，基于PASCAL VOC评估标准进行测试。相比其他先进算法，本方法使SVM分类器的检测速度提升超过50%，准确率最高提升2%。同时，相较于可变形部件模型（DPM）中的等效系统，检测准确率提升约9%。
原文摘要 · Abstract (English)
We present a new wrapper feature selection algorithm for human detection. This algorithm is a hybrid feature selection approach combining the benefits of filter and wrapper methods. It allows the selection of an optimal feature vector that well represents the shapes of the subjects in the images. In detail, the proposed feature selection algorithm adopts the k-fold subsampling and sequential backward elimination approach, while the standard linear support vector machine (SVM) is used as the classifier for human detection. We apply the proposed algorithm to the publicly accessible INRIA and ETH pedestrian full image datasets with the PASCAL VOC evaluation criteria. Compared to other state of the arts algorithms, our feature selection based approach can improve the detection speed of the SVM classifier by over 50% with up to 2% better detection accuracy. Our algorithm also outperforms the equivalent systems introduced in the deformable part model approach with around 9% improvement in the detection accuracy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。