{"id":"W4387829418","doi":"10.1109/igarss52108.2023.10282752","title":"Moving Object Detection by Low-Rank Analysis of Region-Based Correlated Motion Fields","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Robust principal component analysis; Artificial intelligence; Robustness (evolution); Computer vision; Computer science; Object detection; Motion estimation; Principal component analysis; Motion field; Quarter-pixel motion; Exploit; Motion detection; Motion compensation; Pattern recognition (psychology); Noise (video); Motion (physics); Image (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":[0.0007003926,0.0008222512,0.0008226724,0.002050472,0.0002527049,0.0006046676,0.0007226467,0.0005015215,0.0006336283],"category_scores_gemma":[0.00202936,0.0003541789,0.0007370895,0.001252881,0.0004603836,0.0009449264,0.0005216965,0.0006757008,0.0003145606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004357456,"about_ca_system_score_gemma":0.0005862626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003387118,"about_ca_topic_score_gemma":0.003661803,"domain_scores_codex":[0.9994973,0.0001072693,0.00002550825,0.0001173406,0.0001927208,0.00005988887],"domain_scores_gemma":[0.9992852,0.0002463734,0.0001675456,0.00008582493,0.0001740065,0.00004108872],"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":[0.0002938263,0.0001474991,0.003407141,0.0002019513,0.00013143,0.0002675275,0.0001453135,0.1784589,0.1181361,0.01042027,0.002763946,0.685626],"study_design_scores_gemma":[0.000008756143,0.00005068036,0.001769263,0.000009385597,0.0000165012,0.0001033345,0.00001544084,0.9854214,0.009387285,0.002077248,0.001121873,0.00001875594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01697577,0.0002711074,0.9819901,0.00005021037,0.00001758202,0.00002753044,0.00005808861,0.0002670196,0.0003426093],"genre_scores_gemma":[0.3681501,0.000708962,0.6284951,0.0001095363,0.0001277555,0.00007210507,0.0005237101,0.00009883748,0.001713895],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003387118,"threshold_uncertainty_score":0.006734788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01829296585565566,"score_gpt":0.2691507442541142,"score_spread":0.2508577783984586,"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."}}