Abstract

Developing a system for emergency response and rescue to find injured people within images has been an interest to many researchers. The key challenge is to define a proper feature to describe the human body's appearance. Various features are often extracted from low-level data, such as texture and colour (Zhang et al., Sensors. 19(5):1005, 2020). One of the strong features is the Sector Ring Histogram of Oriented Gradients (SRHOG) that has been successfully applied to human detection tasks. Despite good accuracy in finding humans, an SRHOG detection method produces a large amount of false-negative labels. Locating an injured body after a disaster in drone images using an imaging camera remained a challenge. This study presents a new extension to SRHOG, so-called AdSRHOG, to reduce the number of false labels. In our approach, the gradient filters used by SRHOG defined adaptively depending on the corresponding pixel location The proposed feature was used the Support Vector Machine (SVM) algorithm to detect humans on drone images. The experiments showed a significant improvement of up to 54.3% in reducing the false labels. It was also found that the overall accuracy of the human detection process had a notable improvement of 13.1% over a traditional SRHOG detection technique.

摘要

开发紧急响应和救援系统以在图像中找到受伤的人一直是许多研究人员的兴趣所在。关键的挑战是定义一个适当的特征来描述人体的外观。通常从低级数据中提取各种特征，例如纹理和颜色（Zhang等人，Sensors。19（5）：1005，2020）。强大的功能之一是定向梯度扇形环直方图（SRHOG），它已成功应用于人体检测任务。尽管找到人类的准确性很高，但SRHOG检测方法会产生大量的假阴性标签。使用成像相机在无人机图像灾难中定位受伤的身体仍然是一个挑战。这项研究提出了对SRHOG的新扩展，即所谓的AdSRHOG，以减少错误标签的数量。在我们的方法中 SRHOG使用的梯度过滤器根据相应的像素位置进行自适应定义。拟议的功能用于支持向量机（SVM）算法来检测无人机图像上的人员。实验表明，减少假标签的效果显着提高了54.3％。还发现，与传统的SRHOG检测技术相比，人类检测过程的整体准确性显着提高了13.1％。