{"id":"W2796085209","doi":"10.1007/978-3-319-89656-4_38","title":"Real-Time Deep Learning Pedestrians Classification on a Micro-Controller","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Pedestrian detection; Artificial neural network; Software deployment; Raspberry pi; Object detection; Machine learning; Real-time computing; Pedestrian; Pattern recognition (psychology); Embedded system; Operating system; Internet of Things; 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":[0.0002761271,0.0008047022,0.0007195988,0.0003346571,0.0003161227,0.0005388372,0.001222774,0.0005770752,0.006353785],"category_scores_gemma":[0.0005814058,0.0003603493,0.0003002243,0.0003629889,0.0002037387,0.0005774755,0.0006043832,0.001015963,0.001387182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006938947,"about_ca_system_score_gemma":0.0008599402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01145676,"about_ca_topic_score_gemma":0.01967061,"domain_scores_codex":[0.9997758,0.00001474149,0.000005769063,0.0000940362,0.00005836519,0.00005122528],"domain_scores_gemma":[0.9997621,0.00005790285,0.00001437265,0.00004438948,0.00008525002,0.00003599682],"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":[0.001003198,0.000444083,0.003044583,0.00009945166,0.00008616671,0.0001945956,0.00005043027,0.1131174,0.05769604,0.001994481,0.01313846,0.8091311],"study_design_scores_gemma":[0.00001221747,0.0000801503,0.0008793984,0.000003617934,0.00001036033,0.00002868056,0.000007500982,0.9904383,0.007007723,0.0006437699,0.0008815921,0.000006717748],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.153807,0.0005706645,0.8258037,0.0003681545,0.0005812991,0.0001578897,0.0005146858,0.01188044,0.006316292],"genre_scores_gemma":[0.8509912,0.0001247058,0.1402527,0.0001784692,0.00006080686,0.00007659142,0.0005324901,0.0001282622,0.007654859],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01145676,"threshold_uncertainty_score":0.02278012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02611608407897194,"score_gpt":0.2802285155693862,"score_spread":0.2541124314904142,"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."}}