{"id":"W2108841919","doi":"10.1109/ccece.2003.1226111","title":"A novel zoom invariant video object tracking algorithm (ZIVOTA)","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Zoom; Computer vision; Affine transformation; Artificial intelligence; Affine shape adaptation; Invariant (physics); Video tracking; Computer science; Affine combination; Algorithm; Object (grammar); Mathematics; Geometry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001167017,0.0002236885,0.0002754788,0.0001508849,0.0001919674,0.0003825275,0.0009619904,0.00009878461,0.00002430445],"category_scores_gemma":[0.0001456185,0.0001929897,0.0001357406,0.0007283307,0.00004365476,0.000658492,0.0002265677,0.0002306676,0.0001328708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008222274,"about_ca_system_score_gemma":0.0002056829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004722478,"about_ca_topic_score_gemma":0.0001243627,"domain_scores_codex":[0.9980623,0.00007840907,0.0003339462,0.0006164165,0.0004009236,0.0005080698],"domain_scores_gemma":[0.9986809,0.0001992941,0.0000968584,0.0007667353,0.000111492,0.0001446976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000006464551,0.0001973417,0.00054656,0.00002036271,0.00007607737,0.0001890823,0.001214397,0.0009985183,0.02045295,0.1756094,0.0001478191,0.800541],"study_design_scores_gemma":[0.01542093,0.001315874,0.1472837,0.0005839238,0.00007690358,0.003032734,0.0003495122,0.167766,0.3070031,0.3254829,0.02656492,0.005119477],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002065578,0.0001246265,0.9890901,0.001465187,0.0006511446,0.0001455964,0.0000025252,0.0005198416,0.005935421],"genre_scores_gemma":[0.281602,0.00001213238,0.7173403,0.0007681507,0.0001400219,0.00001080271,0.000001155636,0.00001468786,0.0001107231],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7954215,"threshold_uncertainty_score":0.7869893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03444978283359156,"score_gpt":0.2857319413818838,"score_spread":0.2512821585482922,"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."}}