{"id":"W4242606816","doi":"10.1007/s001380050122","title":"Tracking a person with pre-recorded image database and a pan, tilt, and zoom camera","year":2000,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"IBM (Canada); University of Toronto; York University","funders":"","keywords":"Computer vision; Artificial intelligence; Zoom; Tracking (education); Computer science; Tilt (camera); Video tracking; Segmentation; Image (mathematics); Computer graphics (images); Object (grammar); Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002588755,0.0005321895,0.001078806,0.001092773,0.0004931295,0.0005152777,0.0007287265,0.0009104971,0.002263434],"category_scores_gemma":[0.0006119687,0.0002933077,0.0003533618,0.0009927918,0.0001569181,0.0006032285,0.0005742793,0.0004480962,0.001558704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002737504,"about_ca_system_score_gemma":0.0004449806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008547328,"about_ca_topic_score_gemma":0.013568,"domain_scores_codex":[0.9997357,0.00001365272,0.00001205802,0.0001275261,0.00007952895,0.00003157023],"domain_scores_gemma":[0.9997835,0.00002693833,0.00002429747,0.00006239775,0.00006866019,0.00003428211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002670425,0.001084201,0.04192554,0.0004015661,0.0003701352,0.001718937,0.0005001185,0.01214478,0.2255693,0.001293053,0.01215544,0.7001666],"study_design_scores_gemma":[0.0001688611,0.001738272,0.2392718,0.0001014358,0.0005456944,0.01143678,0.0008320311,0.4883298,0.2396706,0.002268481,0.01548377,0.0001522995],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5819572,0.0009934558,0.4007604,0.0003165659,0.0002415934,0.0004376496,0.003773294,0.004780689,0.006739232],"genre_scores_gemma":[0.7966298,0.0008146369,0.1899326,0.0001698587,0.0001153504,0.0001339333,0.00361668,0.00008347293,0.008503698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008547328,"threshold_uncertainty_score":0.01699519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01240692098803086,"score_gpt":0.2956511766217705,"score_spread":0.2832442556337397,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}