{"id":"W2902709485","doi":"10.1155/2018/4815383","title":"Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Technische Universität München; Deutsche Forschungsgemeinschaft","keywords":"Neuromorphic engineering; Computer science; Frame rate; Cluster analysis; Benchmark (surveying); Artificial intelligence; Frame (networking); Computer vision; Low latency (capital markets); Real-time computing; Intelligent transportation system; Wireless sensor network; Latency (audio); Event (particle physics); Tracking (education); Artificial neural network; Engineering; Computer network; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001540337,0.000126725,0.0001817694,0.0001216711,0.0001087346,0.00001456412,0.00004649013,0.00005096368,0.00000163672],"category_scores_gemma":[0.00001305405,0.0001243282,0.00007897919,0.0001393299,0.00002838427,0.0005172785,2.706674e-7,0.0001362781,4.433929e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004723011,"about_ca_system_score_gemma":0.000008527355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.686239e-7,"about_ca_topic_score_gemma":0.00003387645,"domain_scores_codex":[0.9991084,0.00001538027,0.0004839997,0.0001215495,0.0001353206,0.0001353072],"domain_scores_gemma":[0.9993339,0.00009833754,0.0002034126,0.00005362714,0.0002451986,0.00006555526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003672885,0.0000155222,0.000125758,0.0002595464,0.00001289046,0.000009628335,0.0007336931,0.3998884,0.520153,0.00002941366,9.423965e-7,0.07840394],"study_design_scores_gemma":[0.001939856,0.001136348,0.06982152,0.000460879,0.00009643052,0.00001840595,0.0007570106,0.0795207,0.8454192,0.0001253242,0.000477087,0.0002272695],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5841579,0.00007670301,0.4151576,0.000009148264,0.0004011416,0.0001375054,0.000006193033,0.00005168679,0.000002162051],"genre_scores_gemma":[0.9912816,0.00002780225,0.008454751,0.0000147681,0.0001776607,0.000005176354,0.000009070551,0.00002796413,0.000001168068],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4071238,"threshold_uncertainty_score":0.5069957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01889780056279559,"score_gpt":0.2552221026157561,"score_spread":0.2363243020529605,"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."}}