{"id":"W2404978658","doi":"10.1061/9780784479827.092","title":"Project Related Entities Tracking on Construction Sites by Particle Filtering","year":2016,"lang":"en","type":"article","venue":"Construction Research Congress 2016","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Tracking (education); Particle filter; Computer vision; Computer science; Video tracking; Artificial intelligence; Object (grammar); Tracking system; Track (disk drive); Object detection; Window (computing); Pattern recognition (psychology); Filter (signal processing)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006404934,0.0007093732,0.0007722493,0.001728082,0.0005458468,0.0008622914,0.0007580749,0.0007305163,0.0005722395],"category_scores_gemma":[0.001089432,0.0004862688,0.0009256843,0.001976577,0.0002894869,0.0007773012,0.0006632819,0.0007100845,0.000300688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005516785,"about_ca_system_score_gemma":0.0009128876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02150888,"about_ca_topic_score_gemma":0.01498963,"domain_scores_codex":[0.9995269,0.00006427693,0.00001914892,0.0001467342,0.0001794078,0.00006349743],"domain_scores_gemma":[0.9996675,0.00009030027,0.00005799717,0.00004276908,0.0001168215,0.00002461495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000228092,0.0001765654,0.01612724,0.0001280175,0.000179573,0.0002794269,0.0002958381,0.5569959,0.02663755,0.003467227,0.003089613,0.392395],"study_design_scores_gemma":[0.000007246847,0.00002666756,0.003410613,0.00000481833,0.00001887284,0.0000353885,0.00002528781,0.9926645,0.002601943,0.0004503341,0.000742172,0.00001208139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0562492,0.0001643834,0.9416116,0.00005681458,0.00005203634,0.00004515515,0.00008070953,0.0005188751,0.001221244],"genre_scores_gemma":[0.68715,0.0004629096,0.3079008,0.00004179309,0.00003947674,0.0001121176,0.0004931945,0.00005554715,0.003744223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02150888,"threshold_uncertainty_score":0.04276735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06019269248268175,"score_gpt":0.3474326667558729,"score_spread":0.2872399742731911,"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."}}