{"id":"W2055480697","doi":"10.1007/s00138-011-0342-z","title":"Pedestrian tracking using color, thermal and location cue measurements: a DSmT-based framework","year":2011,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bishop's University","funders":"","keywords":"Clutter; Robustness (evolution); Computer vision; Computer science; Artificial intelligence; Particle filter; Tracking (education); Pedestrian; Frame (networking); Filter (signal processing); Radar; Engineering","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":[0.0005694235,0.000828048,0.001562268,0.001553239,0.0005317035,0.001084838,0.001597058,0.001043695,0.001102947],"category_scores_gemma":[0.001095671,0.0005867886,0.001205532,0.001719448,0.0004610505,0.0007511496,0.001053676,0.0007497936,0.0008608457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005246123,"about_ca_system_score_gemma":0.00126965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007862864,"about_ca_topic_score_gemma":0.01003907,"domain_scores_codex":[0.9995896,0.00006501934,0.00001816506,0.0001066044,0.0001593842,0.00006119331],"domain_scores_gemma":[0.9996178,0.00005008077,0.00004200273,0.0000648883,0.0001829523,0.00004228814],"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":[0.0006021746,0.0003714873,0.005003337,0.0002259056,0.0002812897,0.0002357505,0.000129495,0.300763,0.07405364,0.009853664,0.004365385,0.6041149],"study_design_scores_gemma":[0.000009895925,0.00003568547,0.0007827266,0.000005516828,0.00002928394,0.00006504352,0.000009545141,0.9928318,0.003919376,0.0015515,0.000746397,0.00001313734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01367206,0.0002163012,0.9843333,0.00006915293,0.00006890797,0.00003524775,0.0001365142,0.0005040836,0.0009644303],"genre_scores_gemma":[0.3574074,0.0004729773,0.6373283,0.0001258733,0.0001699516,0.0001169099,0.0009069215,0.0001311674,0.003340641],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007862864,"threshold_uncertainty_score":0.01563418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1004799064810504,"score_gpt":0.3476925107248356,"score_spread":0.2472126042437852,"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."}}