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dc.contributor.author | Husnain Shafqat, 01-133182-035 | |
dc.contributor.author | Nabeel khan, 01-133182-071 | |
dc.contributor.author | Waqar Ahmad, 01-133182-131 | |
dc.date.accessioned | 2023-09-25T08:11:22Z | |
dc.date.available | 2023-09-25T08:11:22Z | |
dc.date.issued | 2019 | |
dc.identifier.uri | http://hdl.handle.net/123456789/16241 | |
dc.description | Supervised by Muhammad Yaseen | en_US |
dc.description.abstract | By following to human social distance norms, transmission of COVID-19 may be avoided. It is possible to identify pairs of individuals who are not preserving a social distance limit of roughly six feet between them by using a revolutionary approach developed by us. No assumptions regarding crowd size or pedestrian routes are made in our system. An RGB-D camera and 2-D lidar are used to execute accident navigation in a population and calculate the distance between all recognized individuals in the camera's field of view. In order to determine whether someone has an excessively high temperature, security and medical staff employ a robot-mounted thermal imaging camera. An indoor CCTV camera system might help our mobile robot better identify social distance violations, correctly track people on the ground, and other such tasks. In a range of static and dynamic indoor environments, our technique's performance gains have been shown. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Electrical Engineering, Bahria University Engineering School Islamabad | en_US |
dc.relation.ispartofseries | BEE;P-2422 | |
dc.subject | Electrical Engineering | en_US |
dc.subject | Robots in Covid 19 | en_US |
dc.subject | Raspberry pi 3b | en_US |
dc.title | IOT Covid Bot | en_US |
dc.type | Project Reports | en_US |