{"id":"W3004460637","doi":"10.1016/j.knosys.2020.105594","title":"CNN tracking based on data augmentation","year":2020,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"State Key Laboratory of Robotics and System; National Natural Science Foundation of China; Deutsche Forschungsgemeinschaft","keywords":"Computer science; Robustness (evolution); Artificial intelligence; BitTorrent tracker; Convolutional neural network; Video tracking; Hash function; Benchmark (surveying); Pattern recognition (psychology); Filter (signal processing); Computer vision; Eye tracking; Exploit; Tracking (education); ENCODE; Object (grammar)","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":[],"consensus_categories":[],"category_scores_codex":[0.001621929,0.0002448607,0.0003382541,0.0001403557,0.0001831237,0.0004327993,0.001831199,0.00009386069,0.0000155723],"category_scores_gemma":[0.0003401857,0.0002279128,0.00008286746,0.0007993137,0.00003188127,0.0004842124,0.0001414169,0.0001664395,0.000471583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007643823,"about_ca_system_score_gemma":0.000290447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002808159,"about_ca_topic_score_gemma":0.00001474225,"domain_scores_codex":[0.9971005,0.0007740763,0.0004477576,0.0008917995,0.0004359954,0.0003498142],"domain_scores_gemma":[0.9971908,0.0006978704,0.0001900805,0.001552985,0.0001567542,0.0002114843],"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":[0.0004725335,0.00191679,0.0463382,0.003802251,0.0002775578,0.0003976115,0.005844032,0.1821558,0.0173463,0.01908245,0.066939,0.6554275],"study_design_scores_gemma":[0.001044907,0.0001780732,0.001661707,0.000181775,0.00001035085,0.000001543138,0.00003062226,0.9728234,0.002716283,0.00002380926,0.02103976,0.0002877763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001251752,0.0004565218,0.9891174,0.00162053,0.001746428,0.0003865638,0.00002767314,0.000538952,0.004854203],"genre_scores_gemma":[0.9843228,0.000001438483,0.01397623,0.0009747743,0.0005226347,0.00003114972,0.00008999272,0.00002799421,0.00005294559],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9830711,"threshold_uncertainty_score":0.9294013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1718671060495409,"score_gpt":0.3530485955476173,"score_spread":0.1811814894980764,"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."}}