PMC:7796058 / 48965-49927 JSONTXT

{"project":"LitCovid-PubTator","denotations":[{"id":"344","span":{"begin":88,"end":94},"obj":"Species"},{"id":"345","span":{"begin":96,"end":102},"obj":"Species"},{"id":"346","span":{"begin":129,"end":135},"obj":"Species"},{"id":"347","span":{"begin":266,"end":272},"obj":"Species"},{"id":"348","span":{"begin":724,"end":730},"obj":"Species"},{"id":"349","span":{"begin":811,"end":817},"obj":"Species"},{"id":"350","span":{"begin":876,"end":882},"obj":"Species"},{"id":"351","span":{"begin":456,"end":465},"obj":"Disease"}],"attributes":[{"id":"A344","pred":"tao:has_database_id","subj":"344","obj":"Tax:9606"},{"id":"A345","pred":"tao:has_database_id","subj":"345","obj":"Tax:9606"},{"id":"A346","pred":"tao:has_database_id","subj":"346","obj":"Tax:9606"},{"id":"A347","pred":"tao:has_database_id","subj":"347","obj":"Tax:9606"},{"id":"A348","pred":"tao:has_database_id","subj":"348","obj":"Tax:9606"},{"id":"A349","pred":"tao:has_database_id","subj":"349","obj":"Tax:9606"},{"id":"A350","pred":"tao:has_database_id","subj":"350","obj":"Tax:9606"},{"id":"A351","pred":"tao:has_database_id","subj":"351","obj":"MESH:D006973"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"This section discusses the experimental design for our camera surveillance for counting people, People Density, or the number of people who entered or left a geofence polygon area. For indoor spaces, Physical Distancing rules result in restrictions on the number of people occupying a space. The input for the DL models was online video feeds of fixed cameras focused on the regions of interest defined as IndoorGML cells (e.g., rooms, corridors, lobbies, elevators, stairs, and coffee places). Some cameras might even be able to cover multiple regions of interest (IndoorGML cells), depending on where they are installed and if the spaces are separated by glass walls or windows. An alarm can be triggered by the number of people entering or exiting a region (identified in the camera image) if the density of people exceeds the density of that area. Moreover, the number of people violating physical distancing rules can be identified and reported to the IoCT."}

{"project":"LitCovid-sentences","denotations":[{"id":"T346","span":{"begin":0,"end":180},"obj":"Sentence"},{"id":"T347","span":{"begin":181,"end":291},"obj":"Sentence"},{"id":"T348","span":{"begin":292,"end":494},"obj":"Sentence"},{"id":"T349","span":{"begin":495,"end":680},"obj":"Sentence"},{"id":"T350","span":{"begin":681,"end":851},"obj":"Sentence"},{"id":"T351","span":{"begin":852,"end":962},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"This section discusses the experimental design for our camera surveillance for counting people, People Density, or the number of people who entered or left a geofence polygon area. For indoor spaces, Physical Distancing rules result in restrictions on the number of people occupying a space. The input for the DL models was online video feeds of fixed cameras focused on the regions of interest defined as IndoorGML cells (e.g., rooms, corridors, lobbies, elevators, stairs, and coffee places). Some cameras might even be able to cover multiple regions of interest (IndoorGML cells), depending on where they are installed and if the spaces are separated by glass walls or windows. An alarm can be triggered by the number of people entering or exiting a region (identified in the camera image) if the density of people exceeds the density of that area. Moreover, the number of people violating physical distancing rules can be identified and reported to the IoCT."}