Online growing neural gas for anomaly detection in changing surveillance scenes

• Main Authors: SUN, Qianru, LIU, Hong, HARADA, Tatsuya
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Anomaly detection; Video surveillance; Unsupervised learning; Computer Engineering; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/4454; https://ink.library.smu.edu.sg/context/sis_research/article/5457/viewcontent/Qianru_PR_main.pdf
• Institution: Singapore Management University

Anomaly detection is still a challenging task for video surveillance due to complex environments and unpredictable human behaviors. Most existing approaches train offline detectors using manually labeled data and predefined parameters, and are hard to model changing scenes. This paper introduces a n...