Metrics on Crowd Control with Overhead Video and Vocal Commands
| dc.contributor.advisor | Becker, Aaron T. | |
| dc.contributor.committeeMember | Han, Zhu | |
| dc.contributor.committeeMember | Yao, Yan | |
| dc.creator | Yao, Wei | |
| dc.creator.orcid | 0000-0002-8897-3899 | |
| dc.date.accessioned | 2018-07-10T18:52:19Z | |
| dc.date.available | 2018-07-10T18:52:19Z | |
| dc.date.created | May 2016 | |
| dc.date.issued | 2016-05 | |
| dc.date.submitted | May 2016 | |
| dc.date.updated | 2018-07-10T18:52:19Z | |
| dc.description.abstract | This thesis presents an agent-tracking framework for semi-structured, crowded video. This framework is used to investigate how large numbers of people respond to vocal commands with local feedback and an overhead camera video. We analyze a video showing an overhead view of more than 200 people, each holding an umbrella equipped with red, blue, and green LED lights. The crowd’s motion under the vocal command formed a variety of patterns. We use k-means clustering to separate umbrella from each other. Kalman filtering is used to estimate how each umbrella moves and track their motion path. In particular, we present results on: (1) Automatic segmentation and classification of each umbrella. (2) Swarm’s response time to a simple command. (3) Time constant for a harder command. (4) Comparing accuracy. (5) “Shape-matching” ability. (6) Documenting the position memory. (7) Distribution consensus simulation. | |
| dc.description.department | Electrical and Computer Engineering, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/10657/3212 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | K-means clustering | |
| dc.subject | Vision tracking | |
| dc.subject | Kalman filter | |
| dc.title | Metrics on Crowd Control with Overhead Video and Vocal Commands | |
| dc.type.dcmi | Text | |
| dc.type.genre | Thesis | |
| thesis.degree.college | Cullen College of Engineering | |
| thesis.degree.department | Electrical and Computer Engineering, Department of | |
| thesis.degree.discipline | Electrical Engineering | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science in Electrical Engineering |