{"id":"W4304480610","doi":"10.1109/tnnls.2022.3209918","title":"CLRNet: A Cross Locality Relation Network for Crowd Counting in Videos","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor; Western University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Locality; Computer science; Relation (database); Similarity (geometry); Artificial intelligence; Measure (data warehouse); Pixel; Feature (linguistics); Consistency (knowledge bases); Cosine similarity; Computer vision; Pattern recognition (psychology); Data mining; Image (mathematics)","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":[0.001086022,0.001406666,0.001060269,0.002529944,0.0007370748,0.0007930835,0.002163209,0.001146134,0.002613065],"category_scores_gemma":[0.003619941,0.0005663756,0.0009637718,0.001700624,0.0006035698,0.002205809,0.002196325,0.0009597551,0.0008649267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001312393,"about_ca_system_score_gemma":0.0008373249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009613038,"about_ca_topic_score_gemma":0.009614254,"domain_scores_codex":[0.9993482,0.000137805,0.00002982436,0.0002464848,0.0001679974,0.00006973427],"domain_scores_gemma":[0.9993274,0.0002259147,0.00009359133,0.00008334146,0.0002147885,0.00005494893],"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":[0.0004546529,0.0002587537,0.004064234,0.0002677116,0.0001962702,0.0002994093,0.0003008461,0.4243963,0.01216708,0.01285691,0.01433633,0.5304015],"study_design_scores_gemma":[0.00001070018,0.00004202842,0.0005416512,0.0000154991,0.00002352733,0.00006326583,0.00003178595,0.9897335,0.002642534,0.004610771,0.002269392,0.00001526329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02287062,0.0006612349,0.9696098,0.0002345535,0.0001434419,0.0001595168,0.0006409085,0.002819757,0.002860219],"genre_scores_gemma":[0.5389796,0.0008516696,0.4476408,0.0004577893,0.0002182844,0.0005327963,0.002776483,0.0004188973,0.008123693],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009613038,"threshold_uncertainty_score":0.01911414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02314950838952577,"score_gpt":0.2824192690738697,"score_spread":0.2592697606843439,"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."}}