{"id":"W2954369198","doi":"10.1109/access.2019.2924732","title":"Learning Robust Features for Planar Object Tracking","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; University of Alberta; National Science Foundation","keywords":"Computer science; Artificial intelligence; Robustness (evolution); Convolutional neural network; Computer vision; Deep learning; Motion blur; Feature extraction; Pattern recognition (psychology); Video tracking; Pixel; Eye tracking; Object (grammar); Image (mathematics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0007281562,0.001056353,0.001183999,0.001442801,0.0003525265,0.0007826404,0.001450822,0.001104629,0.001298988],"category_scores_gemma":[0.003169121,0.000473571,0.0007627801,0.001893843,0.0005096777,0.001190508,0.001310032,0.00126467,0.0009202277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007777324,"about_ca_system_score_gemma":0.0007951948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00535141,"about_ca_topic_score_gemma":0.006639373,"domain_scores_codex":[0.9993727,0.00005064752,0.00002486202,0.0002841896,0.0001840402,0.00008359846],"domain_scores_gemma":[0.9992951,0.0001948833,0.0001402425,0.0001687817,0.0001683064,0.00003270947],"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":[0.0001920436,0.00010969,0.001924505,0.00007050406,0.00009463474,0.00009428692,0.00003998395,0.3413972,0.01707128,0.00451304,0.005366853,0.629126],"study_design_scores_gemma":[0.000008027755,0.000027046,0.0005571629,0.000005861185,0.000009771852,0.00004010201,0.000004871937,0.9920576,0.00385984,0.002484412,0.0009377333,0.000007582601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0219963,0.0004464482,0.9740466,0.00008003565,0.00003792411,0.00003644316,0.0003185325,0.002188683,0.0008491118],"genre_scores_gemma":[0.5993837,0.0005807577,0.3909578,0.0002224496,0.0001262064,0.0001431668,0.003836289,0.0003460212,0.004403651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00535141,"threshold_uncertainty_score":0.01064056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05293879379807757,"score_gpt":0.3374745261316309,"score_spread":0.2845357323335533,"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."}}