{"id":"W2100291131","doi":"10.1109/icsmc.2007.4414038","title":"Real-time automatic detection of vandalism behavior in video sequences","year":2007,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of Canada; Concordia University","funders":"","keywords":"Computer science; Graffiti; Computer vision; Artificial intelligence; Object detection; Object (grammar); Feature extraction; Sequence (biology); Phone; Video tracking; Pattern recognition (psychology)","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":[0.0005151331,0.0006618501,0.0006275866,0.002393074,0.0002291477,0.0005863187,0.0007441963,0.000573718,0.0008186108],"category_scores_gemma":[0.002934025,0.0002685665,0.0002552509,0.000593526,0.0002890393,0.0008529307,0.0003596965,0.0005405505,0.0006437439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002339555,"about_ca_system_score_gemma":0.0003004524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001183289,"about_ca_topic_score_gemma":0.0015616,"domain_scores_codex":[0.9992746,0.0001169646,0.00004506897,0.0001632249,0.0003418784,0.00005837132],"domain_scores_gemma":[0.9982545,0.0004797684,0.0004334921,0.0001413929,0.0005863895,0.0001043529],"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":[0.0004406554,0.0001728245,0.007663306,0.0004765098,0.00009127984,0.0003306087,0.0003106439,0.005726942,0.2918513,0.001084784,0.00347424,0.6883768],"study_design_scores_gemma":[0.0001011935,0.0009764523,0.08225094,0.0001559354,0.0001370823,0.00460631,0.0003711927,0.5584664,0.3291728,0.002384418,0.02120799,0.0001692937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1798998,0.001385224,0.8066605,0.0001507071,0.0002333143,0.0003105544,0.0006874893,0.005921007,0.004751421],"genre_scores_gemma":[0.6238159,0.0007831588,0.3708547,0.00007968892,0.0001768433,0.0001742581,0.001219356,0.0001505533,0.002745541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002393074,"threshold_uncertainty_score":0.002738535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02040524256812056,"score_gpt":0.3113138659785102,"score_spread":0.2909086234103897,"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."}}