Using Deep Learning and Low-Cost RGB and Thermal Cameras to Detect Pedestrians in Aerial Images Captured by Multirotor UAV.
Using Deep Learning and Low-Cost RGB and Thermal Cameras to Detect Pedestrians in Aerial Images Captured by Multirotor UAV.
复制标题
DOI：
10.3390/s18072244
复制
发表时间：
2018-07-12
期刊：
影响因子：
--
通讯作者：
Wehrmeister MA
中科院分区：
文献类型：
--
作者：
de Oliveira DC;Wehrmeister MA
在过去的几年里，无人机（UAV）在许多应用中的使用一直在增加，主要是由于这项技术的成本不断下降。人们可以看到无人机在几个民用应用中的使用，如监视和搜索和救援。航拍图像中行人的自动检测是一项具有挑战性的任务。计算视觉系统必须处理UAV捕获的空中图像中的许多可变性来源，例如，行人的低分辨率图像、由于UAV可以移动的自由度而以不同角度捕获的图像、当UAV飞行时相机平台可能经历一些不稳定性等。在这项工作中，我们创建和评估了不同的实现模式识别系统（PRS），旨在自动检测行人在空中拍摄的图像与多旋翼无人机。主要目标是评估在低成本计算平台上运行的不同PRS实现的可行性和适用性，例如，单板计算机，如Raspberry Pi或没有GPU的普通笔记本电脑。为此，我们在特征提取和分类步骤中使用了四种机器学习技术，即Haar级联，LBP级联，HOG + SVM和卷积神经网络（CNN）。为了提高系统的性能（特别是处理时间），并降低误报率，我们应用显着性图（SM）和热图像处理（TIP）的分割和检测步骤的PRS。分类结果显示，CNN是最好的技术，准确率为99.7%，其次是HOG + SVM，准确率为92.3%。在部分遮挡的情况下，CNN显示出71.1%的灵敏度，与当前最先进的技术相比，这可以被认为是一个很好的结果，因为原始图像数据的一部分丢失了。如实验所示，通过将TIP与CNN相结合，PRS可以每秒处理超过两帧（fps），而将TIP与HOG + SVM相结合的PRS能够处理100 fps。值得一提的是，我们的实验表明，在行人检测PRS的设计过程中必须进行权衡分析。更快的实现导致PRS准确度的降低。例如，通过使用HOG + SVM与TIP，PRS表现出最好的性能结果，但获得的准确率比CNN低35个百分点。所获得的结果表明，最佳检测技术（即，CNN）需要更多的计算资源来减少PRS计算时间。因此，这项工作显示和讨论的优点/缺点，每种技术和权衡的情况下，因此，可以使用这样的分析，以改善和定制设计的PRS检测空中图像中的行人。
The use of Unmanned Aerial Vehicles (UAV) has been increasing over the last few years in many sorts of applications due mainly to the decreasing cost of this technology. One can see the use of the UAV in several civilian applications such as surveillance and search and rescue. Automatic detection of pedestrians in aerial images is a challenging task. The computing vision system must deal with many sources of variability in the aerial images captured with the UAV, e.g., low-resolution images of pedestrians, images captured at distinct angles due to the degrees of freedom that a UAV can move, the camera platform possibly experiencing some instability while the UAV flies, among others. In this work, we created and evaluated different implementations of Pattern Recognition Systems (PRS) aiming at the automatic detection of pedestrians in aerial images captured with multirotor UAV. The main goal is to assess the feasibility and suitability of distinct PRS implementations running on top of low-cost computing platforms, e.g., single-board computers such as the Raspberry Pi or regular laptops without a GPU. For that, we used four machine learning techniques in the feature extraction and classification steps, namely Haar cascade, LBP cascade, HOG + SVM and Convolutional Neural Networks (CNN). In order to improve the system performance (especially the processing time) and also to decrease the rate of false alarms, we applied the Saliency Map (SM) and Thermal Image Processing (TIP) within the segmentation and detection steps of the PRS. The classification results show the CNN to be the best technique with 99.7% accuracy, followed by HOG + SVM with 92.3%. In situations of partial occlusion, the CNN showed 71.1% sensitivity, which can be considered a good result in comparison with the current state-of-the-art, since part of the original image data is missing. As demonstrated in the experiments, by combining TIP with CNN, the PRS can process more than two frames per second (fps), whereas the PRS that combines TIP with HOG + SVM was able to process 100 fps. It is important to mention that our experiments show that a trade-off analysis must be performed during the design of a pedestrian detection PRS. The faster implementations lead to a decrease in the PRS accuracy. For instance, by using HOG + SVM with TIP, the PRS presented the best performance results, but the obtained accuracy was 35 percentage points lower than the CNN. The obtained results indicate that the best detection technique (i.e., the CNN) requires more computational resources to decrease the PRS computation time. Therefore, this work shows and discusses the pros/cons of each technique and trade-off situations, and hence, one can use such an analysis to improve and tailor the design of a PRS to detect pedestrians in aerial images.
登录
查看更多内容
影响因子：
7.5
作者：
CORTES, C;VAPNIK, V
通讯作者：
VAPNIK, V
影响因子：
5
作者：
Ammour, Nassim;Alhichri, Haikel;Zuair, Mansour
通讯作者：
Zuair, Mansour
DOI：
10.1109/tpami.2009.167
发表时间：
2010-09-01
影响因子：
23.6
作者：
Felzenszwalb, Pedro F.;Girshick, Ross B.;Ramanan, Deva
通讯作者：
Ramanan, Deva
影响因子：
1.1
作者：
Freund, Y;Schapire, RE
通讯作者：
Schapire, RE
影响因子：
9
作者：
Arel, Itamar;Rose, Derek C.;Karnowski, Thomas P.
通讯作者：
Karnowski, Thomas P.