{"id":"W4230054345","doi":"10.32920/ryerson.14646711","title":"Obstacle detection using Microsoft Kinect","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"RANSAC; Computer science; Computer vision; Artificial intelligence; Segmentation; Obstacle; Point cloud; Software; Point (geometry); MATLAB; Image segmentation; Computer graphics (images); Image (mathematics); 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":[0.0004214152,0.00117289,0.001048638,0.001917119,0.000400893,0.001237732,0.001365394,0.00109217,0.006901164],"category_scores_gemma":[0.0008985302,0.0007921465,0.0009822302,0.0009781249,0.0002966079,0.001331582,0.001463194,0.0008561086,0.003434671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004225085,"about_ca_system_score_gemma":0.001089838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002181444,"about_ca_topic_score_gemma":0.003175084,"domain_scores_codex":[0.998875,0.00006005552,0.0000527305,0.0002328925,0.0007045594,0.00007478922],"domain_scores_gemma":[0.9996854,0.00005205882,0.00004979036,0.00003283703,0.000144886,0.0000349559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006489943,0.0002010403,0.00366168,0.001879192,0.0001362423,0.0006931472,0.0006022339,0.04900196,0.2574908,0.008880178,0.01577299,0.6610315],"study_design_scores_gemma":[0.00008406902,0.0004444169,0.02014765,0.0007609355,0.0001143983,0.001688766,0.0004585004,0.5910023,0.286273,0.005768141,0.09288612,0.0003717288],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03156804,0.001465498,0.9425967,0.0001820281,0.0003361029,0.000361084,0.00227313,0.007662693,0.01355478],"genre_scores_gemma":[0.2321793,0.002699951,0.7392138,0.0002307276,0.00007017568,0.0007067865,0.004116344,0.0007695715,0.02001336],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006901164,"threshold_uncertainty_score":0.02308673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05824360816384602,"score_gpt":0.322409238116959,"score_spread":0.264165629953113,"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."}}