{"id":"W2901512101","doi":"10.1109/access.2018.2881723","title":"Visual Tracking Based on Correlation Filter and Robust Coding in Bilateral 2DPCA Subspace","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discriminative model; Artificial intelligence; Computer science; Subspace topology; Pattern recognition (psychology); Affine transformation; Computer vision; Robustness (evolution); Eye tracking; Coding (social sciences); Generative model; Mathematics; Generative grammar","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":[0.0007939311,0.0008879568,0.001085092,0.001305656,0.0004565093,0.0009931059,0.00119065,0.000947759,0.001474418],"category_scores_gemma":[0.002615807,0.0004450326,0.001132281,0.001929638,0.0006139261,0.001269736,0.00122175,0.001162193,0.0007529326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006875146,"about_ca_system_score_gemma":0.001592718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0139741,"about_ca_topic_score_gemma":0.01002139,"domain_scores_codex":[0.9992892,0.00008630641,0.00003137991,0.0002000392,0.0003227126,0.00007046173],"domain_scores_gemma":[0.9992812,0.0001718789,0.0001046334,0.0001463008,0.0002447112,0.00005132695],"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":[0.0002055066,0.00009101755,0.001249874,0.0001116534,0.00009458589,0.0001022448,0.0001455042,0.2971409,0.03592229,0.01691848,0.004203188,0.6438147],"study_design_scores_gemma":[0.000007291327,0.00003376778,0.0002901842,0.000007333427,0.00001113607,0.00005557972,0.000007173824,0.9919626,0.00402431,0.002094676,0.001489132,0.00001689028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00490008,0.0001816006,0.9937295,0.00005385928,0.00003149245,0.00002641536,0.00005210775,0.0004497466,0.0005752131],"genre_scores_gemma":[0.2692366,0.000818023,0.7231767,0.0002519026,0.0001116177,0.0002869464,0.000892456,0.0002524562,0.004973331],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0139741,"threshold_uncertainty_score":0.02778554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07652545256430476,"score_gpt":0.3552685109375929,"score_spread":0.2787430583732881,"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."}}