Pregled bibliografske jedinice broj: 1036391
Abnormal Crowd Behaviour Recognition in Surveillance Videos
Abnormal Crowd Behaviour Recognition in Surveillance Videos // 2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) / Yetongnon, Kokou (ur.).
Los Alamitos (CA): Institute of Electrical and Electronics Engineers (IEEE), 2019. str. 428-435 doi:10.1109/SITIS.2019.00075 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1036391 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Abnormal Crowd Behaviour Recognition in Surveillance Videos
Autori
Matković, Franjo ; Marčetić, Darijan ; Ribarić, Slobodan
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)
/ Yetongnon, Kokou - Los Alamitos (CA) : Institute of Electrical and Electronics Engineers (IEEE), 2019, 428-435
ISBN
978-1-7281-5686-6
Skup
15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS 2019 )
Mjesto i datum
Napulj, Italija; Sorrento, Italija, 26.11.2019. - 29.11.2019
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Multi-person tracker, Crowd, Crowd behaviour recognition, Motion patterns, Fuzzy logic
Sažetak
The paper presents an approach to crowd behaviour recognition in surveillance videos. The approach is based on a 4-stage pipelined multi-person tracker adapted to microscopic crowd level representation and crowd behaviour recognition by the evaluation of fuzzy logic functions. The multiperson tracker combines a CNN-based detector and an optical flow-based tracker. The following tracker features are used: optical flow and histogram of optical flow orientation at the macroscopic level, and the tracklets and trajectories of a person and/or group of people at the microscopic level. The human interpretation of video sequences (real and/or video sequences obtained by simulators of crowds) is mapped into fuzzy logic predicates and fuzzy functions. Fuzzy logic predicates specify crowd motion patterns at the microscopic level for a person and/or group of people. They are building blocks of fuzzy logic functions which describe different scenarios of characteristic crowd behaviour. The preliminary results of three experiments for a runaway scenario show that the approach supports efficient and robust crowd behaviour recognition in surveillance videos.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Projekti:
HRZZ-IP-2018-01-7619 - Pristup utemeljen na znanju za analizu mnoštva ljudi u nadzornim sustavima (KACAVIS) (Ribarić, Slobodan, HRZZ ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
Citiraj ovu publikaciju:
Časopis indeksira:
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- Conference Proceedings Citation Index - Science (CPCI-S)
- Scopus