Department of Computer and Communication Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan.
Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung 80811, Taiwan.
Sensors (Basel). 2019 Mar 25;19(6):1458. doi: 10.3390/s19061458.
This paper proposes a method to detect humans in the image that is an important issue for many applications, such as video surveillance in smart home and driving assistance systems. A kind of local feature called the histogram of oriented gradients (HOGs) has been widely used in describing the human appearance and its effectiveness has been proven in the literature. A learning framework called boosting is adopted to select a set of classifiers based on HOGs for human detection. However, in the case of a complex background or noise effect, the use of HOGs results in the problem of false detection. To alleviate this, the proposed method imposes a classifier based on weighted contour templates to the boosting framework. The way to combine the global contour templates with local HOGs is by adjusting the bias of a support vector machine (SVM) for the local classifier. The method proposed for feature combination is referred to as biased boosting. For covering the human appearance in various poses, an expectation maximization algorithm is used which is a kind of iterative algorithm is used to construct a set of representative weighted contour templates instead of manual annotation. The encoding of different weights to the contour points gives the templates more discriminative power in matching. The experiments provided exhibit the superiority of the proposed method in detection accuracy.
本文提出了一种在图像中检测人类的方法，这是许多应用程序的重要问题，如智能家居中的视频监控和驾驶辅助系统。一种称为方向梯度直方图 (HOG) 的局部特征已被广泛用于描述人体外观，其有效性在文献中得到了证明。采用一种称为提升的学习框架，基于 HOG 选择一组分类器用于人体检测。然而，在复杂背景或噪声影响的情况下，HOG 的使用会导致误检问题。为了解决这个问题，所提出的方法将基于加权轮廓模板的分类器施加到提升框架中。将全局轮廓模板与局部 HOG 相结合的方法是通过调整局部分类器的支持向量机 (SVM) 的偏差来实现的。用于特征组合的方法称为有偏提升。为了覆盖各种姿势的人体外观，使用了期望最大化算法，这是一种迭代算法，用于构建一组有代表性的加权轮廓模板，而不是手动注释。对轮廓点进行不同权重的编码使模板在匹配时具有更强的判别能力。实验结果表明，所提出的方法在检测精度方面具有优越性。
