{"id":"W2954593156","doi":"10.1007/s11760-019-01528-y","title":"Local null space pursuit for real-time moving object detection in aerial surveillance","year":2019,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Subspace topology; Artificial intelligence; Computer science; Null (SQL); Object detection; Principal component analysis; Computer vision; Norm (philosophy); Aerial image; Linear subspace; Pattern recognition (psychology); Object (grammar); Mathematics; Data mining; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007082641,0.0003791757,0.0005776178,0.000682272,0.0002364118,0.0004961842,0.000607871,0.0004953686,0.0009635359],"category_scores_gemma":[0.002264652,0.0002011343,0.0003288848,0.000657617,0.0005791898,0.000692313,0.0008283862,0.0005544292,0.0003295003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002780941,"about_ca_system_score_gemma":0.0004431331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001132194,"about_ca_topic_score_gemma":0.0009357116,"domain_scores_codex":[0.9996796,0.000102764,0.00001351668,0.00006341756,0.0001094707,0.0000313161],"domain_scores_gemma":[0.9993764,0.0003483365,0.00006577073,0.00005271528,0.0001179221,0.00003894298],"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":[0.0009265545,0.0001938396,0.001956752,0.000310587,0.000110674,0.0001403968,0.0002715625,0.232682,0.1001061,0.0187223,0.002290691,0.6422885],"study_design_scores_gemma":[0.000009800144,0.00007588794,0.0004630055,0.000004408181,0.000009655691,0.00004760499,0.00002786771,0.992148,0.004717205,0.00214216,0.0003480115,0.000006518088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04313841,0.0003079149,0.9556453,0.00008379287,0.00001680191,0.00001236454,0.00002308598,0.0001680334,0.0006043371],"genre_scores_gemma":[0.7435031,0.000494513,0.2518263,0.00006883167,0.00006327171,0.0000746077,0.0001640719,0.00007142524,0.003733702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001132194,"threshold_uncertainty_score":0.003745735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01132179500352911,"score_gpt":0.2749157757072685,"score_spread":0.2635939807037394,"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."}}