{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006533721,0.00008924075,0.0002068044,0.0005059579,0.00007849412,0.00004270457,0.0002581229,0.00009847214,0.00001466365],"category_scores_gemma":[0.0001440243,0.00008217276,0.000160277,0.004260875,0.00001776593,0.0001687645,0.00003919047,0.0001007505,0.00001452519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002305035,"about_ca_system_score_gemma":0.00001962205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003270114,"about_ca_topic_score_gemma":0.0001360155,"domain_scores_codex":[0.9989198,0.0001726487,0.0002397132,0.0002852046,0.0002090671,0.0001735746],"domain_scores_gemma":[0.998956,0.0003875616,0.0001108134,0.0004155848,0.00009311399,0.00003697507],"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":[0.00003265788,0.000120254,0.05494708,0.00006501048,0.0006782184,0.00002983013,0.0005817226,0.2258552,0.01861792,0.0005054096,0.001203148,0.6973636],"study_design_scores_gemma":[0.0001591774,0.0000381642,0.04321864,0.000009869904,0.00004852985,5.247913e-7,0.00001228838,0.9333026,0.02290388,0.0001806759,0.0000296902,0.00009594679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.157437,0.00001885807,0.8414399,0.0001782244,0.0002330907,0.00005221239,9.923671e-7,0.0003457464,0.0002939797],"genre_scores_gemma":[0.9967232,0.00001262086,0.002952882,0.0001150416,0.00001082495,0.00000523304,0.0000128845,0.000005193097,0.0001620708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8392862,"threshold_uncertainty_score":0.3350908,"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."}}