{"id":"W2772308584","doi":"10.1109/smc.2017.8122663","title":"System-level design for human action recognition in 3D scenes","year":2017,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Field-programmable gate array; Computer science; Preprocessor; Artificial intelligence; Gate array; Point (geometry); Action recognition; Chip; Support vector machine; Computer hardware; Computer vision; Action (physics); Feature extraction; Field (mathematics); Embedded system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003832118,0.00008664028,0.0001042405,0.0001359479,0.0006034956,0.0004113826,0.0003298083,0.00006604393,0.00002694005],"category_scores_gemma":[0.00003660152,0.00008292885,0.00004195247,0.00004241017,0.00001735326,0.001192998,0.00004180653,0.00005763607,0.0001352665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006652866,"about_ca_system_score_gemma":0.00002329071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001274762,"about_ca_topic_score_gemma":0.0002042321,"domain_scores_codex":[0.9992464,0.00004738142,0.0001825478,0.0002567964,0.0001032808,0.0001636118],"domain_scores_gemma":[0.9993345,0.00004737546,0.0001467947,0.0003332008,0.00009990249,0.00003828915],"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":[0.00004107157,0.0002021332,0.0004155348,0.0002176781,0.00002533357,0.00001116789,0.0002654462,0.00003466097,0.01676915,0.02309556,0.002490781,0.9564314],"study_design_scores_gemma":[0.008415763,0.001038491,0.07983337,0.001300472,0.00007044017,0.0001265506,0.000714907,0.293196,0.4828823,0.1257357,0.004666406,0.002019559],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04838815,0.00000336628,0.945176,0.0001757438,0.0004721562,0.0003947121,0.000003840301,0.0001647737,0.005221265],"genre_scores_gemma":[0.9444654,0.000004129654,0.05438219,0.00006799292,0.0001503826,0.0001167048,0.00001125009,0.000007056587,0.0007948668],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9544119,"threshold_uncertainty_score":0.4641662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3046113459026033,"score_gpt":0.3559217859667628,"score_spread":0.05131044006415952,"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."}}