Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

作     者：Ali Almagbile 

作者机构：Department of GeographyYarmouk UniversityIrbidJordan 

出 版 物：《Geo-Spatial Information Science》 (地球空间信息科学学报（英文）)

年 卷 期：2019年第22卷第1期

页      面：23-34页

核心收录：

学科分类：0303[法学-社会学] 0709[理学-地质学] 08[工学] 0708[理学-地球物理学] 0705[理学-地理学] 082503[工学-航空宇航制造工程] 0813[工学-建筑学] 0825[工学-航空宇航科学与技术] 0833[工学-城乡规划学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Unmanned Aerial Vehicle(UAV) crowd density corner detection Feature from Accelerated Segment Test(FAST)algorithm clustering analysis 

摘      要：With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings,crowd monitoring has taken a considerable attentions in many disciplines such as psychology,sociology,engineering,and computer vision.This is due to the fact that,monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents(e.***.sports).One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles(UAVs),because UAVs have the capability to acquiring fast,low costs,high-resolution and real-time images over crowd areas.In addition,geo-referenced images can also be provided through integration of on-board positioning sensors(e.g.GPS/IMU)with vision sensors(digital cameras and laser scanner).In this paper,a new testing procedure based on feature from accelerated segment test(FAST)algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions.The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order.A single pixel which takes the ranking number 9(for FAST-9)or 12(for FAST-12)was then compared with the center pixel.Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features.The results show that the proposed algorithms are able to extract crowd features from different UAV images.Overall,the values of Completeness range from 55 to 70%whereas the range of correctness values was 91 to 94%.