{"id":"W4413880304","doi":"10.5194/isprs-archives-xlviii-m-8-2025-1-2025","title":"Multi-Object Tracking in UAV Videos: A YOLOv11 Fusion Method for Detection and Segmentation Optimization","year":2025,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer vision; Artificial intelligence; Segmentation; Computer science; Fusion; Tracking (education); Object detection; Object (grammar); Video tracking; Pattern recognition (psychology); Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00198943,0.0003550685,0.0003842995,0.001406142,0.0009461241,0.0005721112,0.0007551804,0.0001182077,0.000002896798],"category_scores_gemma":[0.001219889,0.0002496174,0.0002312742,0.001057568,0.001133888,0.0005785931,0.0004236476,0.0003786284,5.387731e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007886184,"about_ca_system_score_gemma":0.00011359,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3848956,"about_ca_topic_score_gemma":0.1222509,"domain_scores_codex":[0.9967704,0.0003588292,0.001286158,0.0003455454,0.0008397162,0.0003993441],"domain_scores_gemma":[0.9969341,0.001605969,0.0008486389,0.000285593,0.0002524631,0.00007324746],"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":[0.0001428169,0.000009681,0.0001194166,0.0000774029,0.00005891399,1.00252e-7,0.002436014,0.1093871,0.02033488,0.000002780605,0.000007250609,0.8674237],"study_design_scores_gemma":[0.0009744245,0.00009058748,0.002673195,0.0003183047,0.00004062543,0.0000529756,0.002332704,0.9319121,0.05753592,0.00325557,0.0005769738,0.0002366796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009556813,0.00004977646,0.9846067,0.001102104,0.001998629,0.0009579557,0.00005787598,0.0000788624,0.001591246],"genre_scores_gemma":[0.899415,0.0002222826,0.09977887,0.0004426784,0.00006728024,0.000001505867,0.0000241447,0.00001137791,0.0000368953],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8898581,"threshold_uncertainty_score":0.9999956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02359585486496473,"score_gpt":0.2950047557108231,"score_spread":0.2714089008458583,"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."}}