{"id":"W3202919459","doi":"10.18280/ts.380437","title":"Integration Between Cascade Region-Based Convolutional Neural Network and Bi-Directional Feature Pyramid Network for Live Object Tracking and Detection","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cascade; Artificial intelligence; Computer science; Pyramid (geometry); Convolutional neural network; Object detection; Pattern recognition (psychology); Feature (linguistics); Computer vision; Tracking (education); Feature extraction; Video tracking; Frame (networking); Object (grammar); Mathematics; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006918204,0.0009364808,0.0005960984,0.0008479903,0.0002619649,0.0004405114,0.001215162,0.0006804457,0.001058998],"category_scores_gemma":[0.001061712,0.0003840163,0.0006893257,0.000721404,0.0002776307,0.001472792,0.0007465066,0.0006671072,0.000403271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009290653,"about_ca_system_score_gemma":0.0008296738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01348799,"about_ca_topic_score_gemma":0.01604096,"domain_scores_codex":[0.9995601,0.00004025064,0.00001948871,0.0001607463,0.0001492069,0.00007023908],"domain_scores_gemma":[0.9996712,0.00006123438,0.00003343593,0.00005547021,0.0001539821,0.00002460535],"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":[0.000341351,0.000232689,0.004935514,0.0001639838,0.0002535762,0.0002711441,0.00007870429,0.1610393,0.08965568,0.003267922,0.004670761,0.7350895],"study_design_scores_gemma":[0.000006818756,0.00009554455,0.001511913,0.000006221796,0.00005423397,0.00009388456,0.000008777033,0.9820955,0.01418011,0.0008014386,0.001130932,0.00001456314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0572359,0.001078331,0.9358523,0.0001481335,0.000121039,0.00007605261,0.000167549,0.002785651,0.002534912],"genre_scores_gemma":[0.7460027,0.0008845083,0.2479082,0.0002057879,0.00006414492,0.00008225413,0.0006330348,0.00009407791,0.004125334],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01348799,"threshold_uncertainty_score":0.02681899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03435486891027862,"score_gpt":0.2724292273098361,"score_spread":0.2380743583995575,"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."}}