{"id":"W2045610461","doi":"10.1109/have.2013.6679617","title":"Particle filtering enhanced human tracking on context-aware robotic system","year":2013,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Particle filter; Artificial intelligence; Context (archaeology); Computer vision; Tracking (education); Mechanism (biology); Mobile robot; Feature (linguistics); Tracking system; Object detection; Robot; Kalman filter; Pattern recognition (psychology)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003944926,0.000165768,0.0002401577,0.00005093486,0.0002228468,0.0003873352,0.0005767438,0.00005311879,0.00007721422],"category_scores_gemma":[0.00003087252,0.0001385884,0.00007775412,0.0002211843,0.00002479528,0.0006239497,0.0001070371,0.0001299774,0.0005816951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005632321,"about_ca_system_score_gemma":0.00001422877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001133164,"about_ca_topic_score_gemma":0.00003197248,"domain_scores_codex":[0.9984829,0.0001601464,0.0002962452,0.0004226351,0.0002293968,0.0004086759],"domain_scores_gemma":[0.9989038,0.0001811721,0.00008171214,0.000624455,0.00009463463,0.0001142969],"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":[0.000008138712,0.000206074,0.004030275,0.0002103177,0.0000867067,0.00005829895,0.001974538,0.005243757,0.1744914,0.2673845,0.0005643562,0.5457417],"study_design_scores_gemma":[0.001354557,0.0004893674,0.074911,0.000520793,0.00001121804,0.00004088705,0.0007353284,0.2031991,0.7153705,0.002113366,0.0002462079,0.001007728],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2224397,0.00002481925,0.7710643,0.0002754666,0.0003502045,0.0001982304,1.870374e-7,0.0006024191,0.005044731],"genre_scores_gemma":[0.9898876,8.606217e-7,0.009202101,0.0003668549,0.00007316301,0.00004602035,6.423385e-7,0.00001423613,0.0004085118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7674479,"threshold_uncertainty_score":0.7476708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0451335961340581,"score_gpt":0.2919621574267677,"score_spread":0.2468285612927096,"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."}}