Abstract

ABSTRACTWith 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.g. 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 %.

Highlights

  • Crowd analysis is considered a hot research topic in various fields such as psychology, sociology, engineering and computer vision (Zhan et al 2008; Burkert and Butenuth 2011)

  • A new testing procedure based on feature from accelerated segment test (FAST) algorithms is introduced to detect the crowd features from unmanned aerial vehicles (UAVs) images taken from different camera orientations and positions

  • This paper provides a new testing procedure based on the feature from accelerated segment test (FAST) algorithm for estimation crowd density from UAV images

Read more

Summary

Introduction

Crowd analysis is considered a hot research topic in various fields such as psychology, sociology, engineering and computer vision (Zhan et al 2008; Burkert and Butenuth 2011). Crowd monitoring and management required constructing a framework that provides surveillance and crowd control to respond to the event situation This framework includes three consecutive steps namely sensing, alerting and action (SAA). Sensing is capturing images of crowd areas using a camera (digital/infrared/multi-spectral) mounted onboard of a moving platform, such as unmanned aerial vehicle (UAV). In some places and events, crowd monitoring is necessary to provide safe and peaceful movement to minimize the risk in some incidents This will enhance decision makers using accurate information to guide people in the field. This paper provides a new testing procedure based on the feature from accelerated segment test (FAST) algorithm for estimation crowd density from UAV images. Test and results are provided followed by discussion and conclusions in the last section

Related work
Crowd feature extraction
Proposed new testing procedure
Evaluating the performance of the proposed test
Filtering the crowd features
Mapping the levels of crowd density
Test and results
Results
Conclusions
Notes on contributor
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call