Crowd Counting in Large Surveillance Areas by Fusing Audio and WiFi Sniffing Data
Popular vision-based crowd counting methods suffer from huge costs, limited coverage and high complexity, making it difficult to be applied for large surveillance areas, while emerging WiFi-based methods which are suitable for large surveillance areas incur limited accuracy due to the sparsity and r...
Saved in:
Published in | 2024 International Joint Conference on Neural Networks (IJCNN) pp. 1 - 8 |
---|---|
Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
30.06.2024
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Popular vision-based crowd counting methods suffer from huge costs, limited coverage and high complexity, making it difficult to be applied for large surveillance areas, while emerging WiFi-based methods which are suitable for large surveillance areas incur limited accuracy due to the sparsity and randomness of WiFi sniffing data. Considering the fact that the variations of audio data are spatial-temporally correlated with crowd fluctuations, this paper proposes to fuse audio and WiFi sniffing data for crowd counting by developing a Cross-modal Multi-level Perception Network, termed CMPN. The CMPN can not only extract crowd features from the bimodal data to leverage the temporally continuity for compensating sparse WiFi sniffing data, but also mine the correlation of intra- and inter-modality crowd features for accurate crowd counting. Extensive experiments are conducted in a real campus with the surveillance area of about 4000m 2 , and demonstrate that the CMPN can achieve the mean absolute error of 5.88, resulting in a 22.12% reduction compared to the state-of-the-art WiFi-only method. |
---|---|
AbstractList | Popular vision-based crowd counting methods suffer from huge costs, limited coverage and high complexity, making it difficult to be applied for large surveillance areas, while emerging WiFi-based methods which are suitable for large surveillance areas incur limited accuracy due to the sparsity and randomness of WiFi sniffing data. Considering the fact that the variations of audio data are spatial-temporally correlated with crowd fluctuations, this paper proposes to fuse audio and WiFi sniffing data for crowd counting by developing a Cross-modal Multi-level Perception Network, termed CMPN. The CMPN can not only extract crowd features from the bimodal data to leverage the temporally continuity for compensating sparse WiFi sniffing data, but also mine the correlation of intra- and inter-modality crowd features for accurate crowd counting. Extensive experiments are conducted in a real campus with the surveillance area of about 4000m 2 , and demonstrate that the CMPN can achieve the mean absolute error of 5.88, resulting in a 22.12% reduction compared to the state-of-the-art WiFi-only method. |
Author | Jia, Bing Guo, Rui Hao, Lifei Huang, Baoqi |
Author_xml | – sequence: 1 givenname: Rui surname: Guo fullname: Guo, Rui email: 32109005@mail.imu.edu.cn organization: Inner Mongolia University,China – sequence: 2 givenname: Baoqi surname: Huang fullname: Huang, Baoqi email: cshbq@imu.edu.cn organization: Inner Mongolia University,China – sequence: 3 givenname: Lifei surname: Hao fullname: Hao, Lifei email: cshlf@imu.edu.cn organization: Inner Mongolia University,China – sequence: 4 givenname: Bing surname: Jia fullname: Jia, Bing email: jiabing@imu.edu.cn organization: Inner Mongolia University,China |
BookMark | eNqFjsFKw0AURZ-i0Fb7By7eDzS-mcmknWVJDVWkUFpwWUbzUp7Eicw0Lf17Keja1YVzzuKO4CZ0gQFQUaYUucfnl3K1KmjmXKZJ55miwipr7BWM3dTNjCVjnVH6GoZaFWqS5zQdwCilTyJtnDNDWJexO9VYdn04SNijBHz1cc-46eORpW19-GCcR_YJ389Y9elSzftaOvShxjepBDdBmubCF_7g7-G28W3i8e_ewUP1tC2XE2Hm3XeULx_Pu7-r5h_9A8-dQ0o |
ContentType | Conference Proceeding |
DBID | 6IE 6IH CBEJK RIE RIO |
DOI | 10.1109/IJCNN60899.2024.10651535 |
DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan (POP) 1998-present by volume IEEE Xplore All Conference Proceedings IEL IEEE Proceedings Order Plans (POP) 1998-present |
DatabaseTitleList | |
Database_xml | – sequence: 1 dbid: RIE name: IEL url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Computer Science |
EISBN | 9798350359312 |
EISSN | 2161-4407 |
EndPage | 8 |
ExternalDocumentID | 10651535 |
Genre | orig-research |
GrantInformation_xml | – fundername: National Natural Science Foundation of China funderid: 10.13039/501100001809 – fundername: Natural Science Foundation of Inner Mongolia funderid: 10.13039/501100004763 |
GroupedDBID | 6IE 6IF 6IH 6IK 6IL 6IM 6IN AAJGR ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IJVOP IPLJI OCL RIE RIL RIO RNS |
ID | FETCH-ieee_primary_106515353 |
IEDL.DBID | RIE |
IngestDate | Wed Sep 18 05:50:09 EDT 2024 |
IsPeerReviewed | false |
IsScholarly | false |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-ieee_primary_106515353 |
ParticipantIDs | ieee_primary_10651535 |
PublicationCentury | 2000 |
PublicationDate | 2024-June-30 |
PublicationDateYYYYMMDD | 2024-06-30 |
PublicationDate_xml | – month: 06 year: 2024 text: 2024-June-30 day: 30 |
PublicationDecade | 2020 |
PublicationTitle | 2024 International Joint Conference on Neural Networks (IJCNN) |
PublicationTitleAbbrev | IJCNN |
PublicationYear | 2024 |
Publisher | IEEE |
Publisher_xml | – name: IEEE |
SSID | ssj0023993 |
Score | 3.8473034 |
Snippet | Popular vision-based crowd counting methods suffer from huge costs, limited coverage and high complexity, making it difficult to be applied for large... |
SourceID | ieee |
SourceType | Publisher |
StartPage | 1 |
SubjectTerms | Accuracy audio sensor Correlation Costs Crowd counting deep learning Fluctuations Fuses multi-modal fusion Neural networks Surveillance WiFi sniffing |
Title | Crowd Counting in Large Surveillance Areas by Fusing Audio and WiFi Sniffing Data |
URI | https://ieeexplore.ieee.org/document/10651535 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LS8QwEB7cPXlaHxUfq8zBa2tNu7E9LtWyLlqUVdzb0iZZKUIqSyvorzfTx4qi4C0EMhkSwmQm35cP4JQzYa7lXNgpk4IoOczOOA9txSX3QxGoZUoF_duETx796Xw0b8nqNRdGKVWDz5RDzfotXxaiolKZOeEk3O2NetALXNaQtdbZFUXaDqrjhmfX0yhJOD1qmSSQ-U439puKSh1E4gEk3fQNduTFqcrMER8_fmb8t39bYH3x9fBuHYm2YUPpHRh0gg3Ynt9duI9M0i0xavUhMNd4Q0hwnFWrN0X6Q2RpTDh1zN4xJlD8M44rmReYaolPeZzjTFOZx_RfpmVqwTC-eogmNrm5eG2-rlh0Hnp70NeFVvuAHhfeknsq85VJtlgaMF-eeyPX3AMufM7kAVi_mjj8o_8INmnBG0zdEPrlqlLHJnCX2Um9YZ9aM5py |
link.rule.ids | 310,311,786,790,795,796,802,27958,55109 |
linkProvider | IEEE |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1dS8MwFL3ofNCn-THxY-p98LV1pm1cH0e1dLMryibubbRJNorQyWgF_fXmtutEUfAtBBIuCeHk3pyTA3DJmdDXci6MmElBkhxmJJy7huKS267oqllMBf1hxIMnezBxJiuxeqmFUUqV5DNlUrN8y5cLUVCpTJ9wMu62nE3Y0kDfcSu51jq_IqytyTod96o_8KKI07OWTgOZbdajv_molDDiNyGqA6jYIy9mkSem-PjxN-O_I9yF1pdiDx_WWLQHGyrbh2Zt2YCrE3wAj55OuyV6K4cITDMMiQuOo2L5psiBiGbqEVMdk3f0iRY_x14h0wXGmcTn1E9xlFGhR_ffxnncgrZ_N_YCg8KcvlafV0zrCK1DaGSLTB0BWlxYM26pxFY63WJxl9ny2nI6-iZwY3Mmj6H16xQnf_RfwHYwHobTsB_dn8IOLX7FsGtDI18W6kzDeJ6cl5v3CbFEncg |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2024+International+Joint+Conference+on+Neural+Networks+%28IJCNN%29&rft.atitle=Crowd+Counting+in+Large+Surveillance+Areas+by+Fusing+Audio+and+WiFi+Sniffing+Data&rft.au=Guo%2C+Rui&rft.au=Huang%2C+Baoqi&rft.au=Hao%2C+Lifei&rft.au=Jia%2C+Bing&rft.date=2024-06-30&rft.pub=IEEE&rft.eissn=2161-4407&rft.spage=1&rft.epage=8&rft_id=info:doi/10.1109%2FIJCNN60899.2024.10651535&rft.externalDocID=10651535 |