{"id":"W1986429310","doi":"10.1007/s11265-010-0540-3","title":"Automatic Detection of Object of Interest and Tracking in Active Video","year":2010,"lang":"en","type":"article","venue":"Journal of Signal Processing Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Initialization; Video tracking; AdaBoost; Pattern recognition (psychology); Classifier (UML); Outlier; Salient; Object detection; Tracking (education); Object (grammar)","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":[0.002209349,0.00009526202,0.0004003475,0.0003463042,0.00003703847,0.00009531226,0.0002913152,0.0000760542,9.319049e-7],"category_scores_gemma":[0.0001925556,0.00007577305,0.0000574643,0.0003733682,0.00005931544,0.0008341684,0.00003259367,0.0003624209,1.561155e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002384802,"about_ca_system_score_gemma":0.0001315591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004415114,"about_ca_topic_score_gemma":0.00004805584,"domain_scores_codex":[0.9985858,0.0001785011,0.0007562445,0.0001212237,0.00023479,0.0001234264],"domain_scores_gemma":[0.9979783,0.0002911678,0.001231098,0.0001060057,0.0003475718,0.00004585145],"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":[0.00002630218,0.00005254191,0.004598056,0.0005708009,0.00002098069,0.00001406828,0.001914575,0.0001498039,0.4631622,0.00008050851,7.738128e-7,0.5294093],"study_design_scores_gemma":[0.001871898,0.001048004,0.1650977,0.006568023,0.00004565334,0.001924208,0.001457535,0.2751621,0.5432186,0.003181136,0.00004155635,0.0003836262],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8141791,0.0004461722,0.1849315,0.00002056319,0.0003184329,0.0000574816,3.272526e-7,0.000009221862,0.0000372002],"genre_scores_gemma":[0.9938388,0.000005166645,0.006057766,0.000003948663,0.00008479832,0.000001119694,3.162968e-8,0.000006605495,0.000001784589],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5290257,"threshold_uncertainty_score":0.3089935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03947942196863002,"score_gpt":0.3100770123080309,"score_spread":0.2705975903394008,"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."}}