Human detection using speeded-up robust features and support vector machine from aerial images

Buhari U. Umar, J. Agajo, A. Aliyu, J. Kolo, Olakunle S. Owolabi, Olayemi Mikail Olaniyi
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引用次数: 5

Abstract

Human detection from an aerial image has attracted wide attention due to its vast area of application such as in surveillance, search and rescue operation, and for visual understanding of the image. Unlike object detection, human detection from an aerial image is a challenging classification problem because of different posture appearance of human in an image. More so, at high altitude human shape appear deformed. Different features selection and different algorithm have been proposed. Although effective, but limited due to, characteristic of human posture in an image. In order to address this problem, this research proposed a Speeded-Up Robust feature selection and SVM for human detection from an aerial image due to computational speed and robustness of the SURF feature. This approach would help in better human detection from aerial images irrespective of position and movement for either rescue or surveillance mission. Aerial images were acquired preprocess and segmented using Otsu segmentation. A database comprises of two hundred images was created; 70 percent (140 images) of it was used in training the classifier and 30 percent (60 images) for testing the classifier. Accuracy of 50%, specificity of 57.1%, sensitivity of 46.2% and precision of 66.7% was achieved. These results can be used for a better human detection from an aerial image irrespective of the position or movement.
基于加速鲁棒特征和支持向量机的航空图像人体检测
航拍图像的人体检测由于其在监视、搜救、图像视觉理解等方面的广泛应用而受到了广泛的关注。与目标检测不同,航拍图像中的人体检测是一个具有挑战性的分类问题,因为图像中人体的姿态外观不同。更严重的是,在高海拔地区,人的形状会出现变形。提出了不同的特征选择和不同的算法。虽然有效,但由于图像中人体姿势的特征而受到限制。为了解决这一问题,本研究利用SURF特征的计算速度和鲁棒性,提出了一种用于航空图像人体检测的加速鲁棒特征选择和支持向量机。这种方法将有助于更好地从航空图像中进行人类检测,无论其位置和运动如何,都可以用于救援或监视任务。对航拍图像进行预处理,并采用Otsu分割进行分割。创建了一个包含200幅图像的数据库;其中70%(140张图片)用于训练分类器,30%(60张图片)用于测试分类器。准确度50%,特异度57.1%,灵敏度46.2%,精密度66.7%。这些结果可以用于更好地从航空图像中进行人类检测,而不考虑位置或运动。
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