{"id":"W2916102438","doi":"10.1007/s00521-019-04057-4","title":"Pedestrian detection via deep segmentation and context network","year":2019,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; Segmentation; Minimum bounding box; Context (archaeology); Pedestrian detection; Artificial intelligence; Feature (linguistics); Pedestrian; Bounding overwatch; Pattern recognition (psychology); Deep learning; Spatial contextual awareness; Machine learning; Computer vision; Image (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":[0.0003807227,0.001269903,0.001447338,0.001469186,0.0006227882,0.0008655458,0.001373727,0.001247591,0.002425392],"category_scores_gemma":[0.0007477639,0.00082914,0.0009490281,0.001317466,0.000412722,0.0010067,0.001387426,0.00136126,0.001139505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008189786,"about_ca_system_score_gemma":0.001201849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01389758,"about_ca_topic_score_gemma":0.02726633,"domain_scores_codex":[0.9996291,0.00003483168,0.00001035371,0.0001693181,0.00006690926,0.00008950965],"domain_scores_gemma":[0.9997748,0.00004826698,0.00002530153,0.00004277926,0.0000746502,0.00003432542],"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":[0.0006568767,0.000363252,0.003482342,0.0001245376,0.0001364066,0.0001954634,0.0000845402,0.06003143,0.04080161,0.006332376,0.008678388,0.8791127],"study_design_scores_gemma":[0.0000102314,0.00005766455,0.00140524,0.00001638269,0.00003922223,0.00009379163,0.00001501827,0.9841925,0.00873052,0.003945339,0.001480925,0.0000133393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06909907,0.001568244,0.920463,0.0003322742,0.0002407739,0.00009472582,0.0004062777,0.004110627,0.003684907],"genre_scores_gemma":[0.6317208,0.0009040816,0.355777,0.0004367519,0.0002198732,0.0001077314,0.001207926,0.0002656612,0.009360312],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01389758,"threshold_uncertainty_score":0.02763337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01357085219647437,"score_gpt":0.2764013181841696,"score_spread":0.2628304659876953,"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."}}