Anomaly Detection in Unsupervised Surveillance Setting Using Ensemble of Multimodal Data with Adversarial Defense

Chowdhury, Sayeed Shafayet; Islam, Kazi Mejbaul; Noor, Rouhan

doi:10.1109/icmla51294.2020.00039

Search citation statements

Order By: Relevance

Paper Sections

Select...

Approximate Learning and Inference1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2024

Publication Types

Select...

Article3

Other1

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To initialize the model parameters with good values, a greedy layer-wise pretraining strategy is adopted. This strategy involves learning a stack of modified Restricted Boltzmann Machines (RBMs) [11].…”

Section: Approximate Learning and Inferencementioning

confidence: 99%

Anomaly detection by using multimodal deep learning

Jiang,

Wan,

Wang

et al. 2023

International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023)

View full text Add to dashboard Cite

Section: Approximate Learning and Inferencementioning

confidence: 99%

Anomaly detection by using multimodal deep learning

Jiang,

Wan,

Wang

et al. 2023

International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023)

View full text Add to dashboard Cite

Anomaly detection using edge computing in video surveillance system: review

Patrikar

Parate

2022

Int J Multimed Info Retr

View full text Add to dashboard Cite

The current concept of smart cities influences urban planners and researchers to provide modern, secured and sustainable infrastructure and gives a decent quality of life to its residents. To fulfill this need, video surveillance cameras have been deployed to enhance the safety and well-being of the citizens. Despite technical developments in modern science, abnormal event detection in surveillance video systems is challenging and requires exhaustive human efforts. In this paper, we focus on evolution of anomaly detection followed by survey of various methodologies developed to detect anomalies in intelligent video surveillance. Further, we revisit the surveys on anomaly detection in the last decade. We then present a systematic categorization of methodologies for anomaly detection. As the notion of anomaly depends on context, we identify different objects-of-interest and publicly available datasets in anomaly detection. Since anomaly detection is a time-critical application of computer vision, we explore the anomaly detection using edge devices and approaches explicitly designed for them. The confluence of edge computing and anomaly detection for real-time and intelligent surveillance applications is also explored. Further, we discuss the challenges and opportunities involved in anomaly detection using the edge devices.

show abstract