论文标题
空天空视频中自动检测的时空处理
Spatio-Temporal Processing for Automatic Vehicle Detection in Wide-Area Aerial Video
论文作者
论文摘要
航空视频中的车辆检测通常需要后处理以消除虚假检测。本文提出了一种时空处理方案，可通过用多个邻居的滞后性磁滞阈值替换前景像素分类的现有检测算法的阈值步骤来提高自动检测性能。所提出的方案还执行空间后处理，其中包括形态的开口和闭合以形状并修剪检测到的物体，以及时间后处理，以进一步减少错误的检测。我们评估了在两个本地空中视频数据集和一个停车车数据集上提议的空间处理的性能，以及在五个本地空中视频数据集和一个公共数据集上提议的时空处理方案的性能。实验评估表明，在七个数据集评估时，提出的方案改善了九种算法中每种算法的车辆检测性能。总体而言，提出的时空处理方案的使用将平均F得分提高到0.8以上，并在假阳性中平均降低了83.8％。
Vehicle detection in aerial videos often requires post-processing to eliminate false detections. This paper presents a spatio-temporal processing scheme to improve automatic vehicle detection performance by replacing the thresholding step of existing detection algorithms with multi-neighborhood hysteresis thresholding for foreground pixel classification. The proposed scheme also performs spatial post-processing, which includes morphological opening and closing to shape and prune the detected objects, and temporal post-processing to further reduce false detections. We evaluate the performance of the proposed spatial processing on two local aerial video datasets and one parking vehicle dataset, and the performance of the proposed spatio-temporal processing scheme on five local aerial video datasets and one public dataset. Experimental evaluation shows that the proposed schemes improve vehicle detection performance for each of the nine algorithms when evaluated on seven datasets. Overall, the use of the proposed spatio-temporal processing scheme improves average F-score to above 0.8 and achieves an average reduction of 83.8% in false positives.
