{"id":"W2911372736","doi":"10.1007/978-3-030-11021-5_28","title":"VisDrone-SOT2018: The Vision Meets Drone Single-Object Tracking Challenge Results","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Clutter; BitTorrent tracker; Drone; Video tracking; Tracking (education); Benchmark (surveying); Eye tracking; Object (grammar); Bounding overwatch; Radar; Cartography","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","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.005019418,0.0008576543,0.0009055729,0.0007705566,0.0005643769,0.001269992,0.005910474,0.0005400089,0.00001058087],"category_scores_gemma":[0.0003866219,0.0006266321,0.0002987046,0.0008638144,0.0008413224,0.001126443,0.001966629,0.001533401,0.0001621663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003717915,"about_ca_system_score_gemma":0.0006059754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003433588,"about_ca_topic_score_gemma":0.0002012294,"domain_scores_codex":[0.9931113,0.0002515674,0.0009896663,0.00264496,0.001844919,0.001157602],"domain_scores_gemma":[0.9928735,0.002461184,0.0006830802,0.003428554,0.0003691832,0.0001845303],"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":[0.00002520068,0.00006259481,0.00001269317,0.00004405379,0.00002112338,0.00009559286,0.001527908,0.01202367,0.0004870264,0.006060424,0.00005518457,0.9795845],"study_design_scores_gemma":[0.003478778,0.003669173,0.002337045,0.004626001,0.00006684466,0.0006183633,0.000002230563,0.6057209,0.009660592,0.2585256,0.1060237,0.005270829],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001072401,0.001944918,0.9729964,0.006187789,0.005599492,0.0006934063,0.00000928553,0.0003011574,0.01216034],"genre_scores_gemma":[0.694654,0.0004544571,0.2988694,0.002461151,0.002273421,0.0000138421,0.00001443761,0.000137788,0.001121427],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9743137,"threshold_uncertainty_score":0.9997668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03775203663814282,"score_gpt":0.2911304752830152,"score_spread":0.2533784386448724,"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."}}