{"id":"W2787176632","doi":"10.1109/crv.2017.55","title":"Person Following Robot Using Selected Online Ada-Boosting with Stereo Camera","year":2017,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Computer vision; Artificial intelligence; Robustness (evolution); Robot; Boosting (machine learning); Mobile robot; Social robot; Stereo camera; Task (project management); Robot control; Engineering","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":[0.001305882,0.000879341,0.001435264,0.0007683951,0.0004692895,0.0005593286,0.001565686,0.0008319205,0.001568235],"category_scores_gemma":[0.001308123,0.0004180773,0.0007942772,0.0005253316,0.000256598,0.0005484923,0.0007000627,0.0007679006,0.001453827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006127313,"about_ca_system_score_gemma":0.0009197608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007528196,"about_ca_topic_score_gemma":0.008835295,"domain_scores_codex":[0.999243,0.0001282195,0.00002492974,0.000268261,0.0002218914,0.000113667],"domain_scores_gemma":[0.9993653,0.00009300531,0.00006035331,0.0001431389,0.0002617264,0.00007642635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006101829,0.0006318546,0.006051552,0.0001064751,0.0001704806,0.0001888687,0.00009782843,0.1273895,0.02550301,0.001125168,0.01364633,0.8244787],"study_design_scores_gemma":[0.00002236691,0.0001069058,0.001332269,0.000007006586,0.00001597254,0.000112458,0.0000157305,0.9909395,0.005096071,0.0006919145,0.001649876,0.00001000306],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08606082,0.0007343387,0.901139,0.0001926667,0.0002166968,0.0001895231,0.000308264,0.007818559,0.003340089],"genre_scores_gemma":[0.4964045,0.0001722938,0.4972844,0.0003316833,0.0000720347,0.00009557493,0.001212078,0.0002542532,0.004173214],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007528196,"threshold_uncertainty_score":0.01496875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09113412998519271,"score_gpt":0.3395870362719064,"score_spread":0.2484529062867137,"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."}}