{"id":"W4206935206","doi":"10.17762/de.vol2022iss1.8717","title":"Detection and Tracking of Moving Object-Using Kalman Filter Enhancement (KF) by Grasshopper Optimization Algorithm","year":2022,"lang":"en","type":"article","venue":"Design Engineering","topic":"Religion and Sociopolitical Dynamics in Nigeria","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Kalman filter; Computer vision; Computer science; Artificial intelligence; Video tracking; Noise (video); Object detection; Algorithm; Object (grammar); Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005131192,0.0008616565,0.001019032,0.0006403298,0.0004325049,0.0006884541,0.0006804431,0.0008682478,0.001237713],"category_scores_gemma":[0.001108114,0.0003732013,0.0008395739,0.0005634202,0.0004598739,0.0009352303,0.000494619,0.000617278,0.0003973907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006332643,"about_ca_system_score_gemma":0.001209541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01447897,"about_ca_topic_score_gemma":0.007579691,"domain_scores_codex":[0.9993966,0.00008263287,0.0000402101,0.0002324269,0.0001902183,0.00005781719],"domain_scores_gemma":[0.9997146,0.00008660301,0.00004841047,0.00002051565,0.0001180679,0.00001181594],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001748419,0.00007255977,0.001644026,0.0002509719,0.0002029729,0.0001279652,0.0002291216,0.6280712,0.0208868,0.004991061,0.00225189,0.3410965],"study_design_scores_gemma":[0.00000939152,0.00003530282,0.0004874259,0.000007221192,0.00001835809,0.00003820146,0.000009606762,0.9953219,0.002699834,0.000532641,0.0008271141,0.00001293936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0111908,0.0003786024,0.9867284,0.00004752944,0.00003085991,0.00002865973,0.0000188762,0.000539846,0.001036504],"genre_scores_gemma":[0.4685179,0.0008688252,0.5246047,0.0001277236,0.00006552663,0.0001591493,0.0002338326,0.000117436,0.005304897],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01447897,"threshold_uncertainty_score":0.0287894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01683897159970369,"score_gpt":0.2604011565455878,"score_spread":0.2435621849458841,"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."}}