{"id":"W2155994391","doi":"10.1109/tcsvt.2005.857311","title":"Voting-based simultaneous tracking of multiple video objects","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Video tracking; Segmentation; Object (grammar); Coding (social sciences); Feature extraction; Object detection; Feature (linguistics); Pattern recognition (psychology); Mathematics","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":[0.001396906,0.0005781262,0.001167496,0.00104405,0.0004427312,0.0008318068,0.001779986,0.0006902602,0.0008758415],"category_scores_gemma":[0.003204559,0.0004946341,0.0005684009,0.0009189567,0.0004432379,0.001403504,0.0008621086,0.000545671,0.0004494859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005160017,"about_ca_system_score_gemma":0.0006623464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002679391,"about_ca_topic_score_gemma":0.003272898,"domain_scores_codex":[0.9985221,0.0002133415,0.00007980887,0.0004253732,0.0006331741,0.0001263659],"domain_scores_gemma":[0.998659,0.0004612835,0.0001580576,0.0002537032,0.0004021158,0.00006569632],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004497014,0.00009836513,0.002826416,0.0001125451,0.0001021022,0.0001372672,0.0002117815,0.04174738,0.1319429,0.004906987,0.001044959,0.8164196],"study_design_scores_gemma":[0.00005180075,0.0002635335,0.003682038,0.0000181506,0.00008531952,0.0003129646,0.00004593032,0.9182181,0.06861079,0.002955702,0.005703553,0.00005217896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02442071,0.0001662007,0.9739028,0.00003210034,0.00005011012,0.00004547587,0.00002280914,0.0004885288,0.0008713687],"genre_scores_gemma":[0.4685055,0.0002113198,0.5269608,0.00006827961,0.00007517852,0.0001097724,0.0002091437,0.0001177513,0.00374231],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002679391,"threshold_uncertainty_score":0.007387638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02773922430536899,"score_gpt":0.2748423860241719,"score_spread":0.2471031617188029,"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."}}