{"id":"W4406110370","doi":"10.1109/tmc.2025.3526573","title":"FastTuner: Fast Resolution and Model Tuning for Multi-Object Tracking in Edge Video Analytics","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"China Scholarship Council","keywords":"Computer science; Analytics; Video tracking; Enhanced Data Rates for GSM Evolution; Tracking (education); Object (grammar); Computer vision; Artificial intelligence; Data mining","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":[0.001303655,0.00142151,0.0009768277,0.0007580897,0.0006080547,0.001189878,0.002310881,0.001151902,0.002124284],"category_scores_gemma":[0.006132001,0.0006657928,0.0005772394,0.0006507505,0.0005496329,0.002430011,0.001959973,0.002151177,0.001229409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007000149,"about_ca_system_score_gemma":0.001261336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007264944,"about_ca_topic_score_gemma":0.009256356,"domain_scores_codex":[0.9994318,0.00007721727,0.00002327255,0.0002045716,0.0001810963,0.00008203556],"domain_scores_gemma":[0.9988305,0.0005034449,0.0001064641,0.0002917122,0.0001777008,0.00009026779],"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":[0.0004881906,0.0004531034,0.004646835,0.0002065705,0.000132211,0.0002310177,0.0002924112,0.4565536,0.03679041,0.004204827,0.01311388,0.482887],"study_design_scores_gemma":[0.00001378578,0.00003026496,0.0002652069,0.000006413771,0.000005238513,0.00003403875,0.00001576665,0.9932827,0.004340175,0.001159398,0.0008362479,0.00001077025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02858255,0.0005879021,0.9537627,0.0001623702,0.00008918032,0.0000993479,0.0001483111,0.01517761,0.001390028],"genre_scores_gemma":[0.4859425,0.0004110181,0.5086015,0.000391432,0.00006296473,0.0002170592,0.0008845208,0.001431518,0.00205749],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007264944,"threshold_uncertainty_score":0.0144453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05133420868989664,"score_gpt":0.3437616184360394,"score_spread":0.2924274097461428,"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."}}