Robust anomaly detection in videos using multilevel representations

Hung Vu, Tu Dinh Nguyen , Trung Le, Wei Luo, Dinh Phung

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Detecting anomalies in surveillance videos has long been an important but unsolved problem. In particular, many existing solutions are overly sensitive to (often ephemeral) visual artifacts in the raw video data, resulting in false positives and fragmented detection regions. To overcome such sensitivity and to capture true anomalies with semantic significance, one natural idea is to seek validation from abstract representations of the videos. This paper introduces a framework of robust anomaly detection using multilevel representations of both intensity and motion data. The framework consists of three main components: 1) representation learning using Denoising Autoencoders, 2) level-wise representation generation using Conditional Generative Adversarial Networks, and 3) consolidating anomalous regions detected at each representation level. Our proposed multilevel detector shows a significant improvement in pixel-level Equal Error Rate, namely 11.35%, 12.32% and 4.31% improvement in UCSD Ped 1, UCSD Ped 2 and Avenue datasets respectively. In addition, the model allowed us to detect mislabeled anomalies in the UCDS Ped 1.
Original languageEnglish
Title of host publicationProceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence
EditorsPascal Van Hentenryck, Zhi-Hua Zhou
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages5216-5223
Number of pages8
ISBN (Electronic)9781577358091
DOIs
Publication statusPublished - 2019
EventAAAI conference on Artificial Intelligence 2019 - Honolulu, United States of America
Duration: 27 Jan 20191 Feb 2019
Conference number: 33rd
https://aaai.org/Conferences/AAAI-19/

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAAAI Press
Number1
Volume33
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

ConferenceAAAI conference on Artificial Intelligence 2019
Abbreviated titleAAAI 2019
CountryUnited States of America
CityHonolulu
Period27/01/191/02/19
Internet address

Cite this

Vu, H., Nguyen , T. D., Le, T., Luo, W., & Phung, D. (2019). Robust anomaly detection in videos using multilevel representations. In P. Van Hentenryck, & Z-H. Zhou (Eds.), Proceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence (pp. 5216-5223). [2579] (Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 33, No. 1). Palo Alto CA USA: Association for the Advancement of Artificial Intelligence (AAAI). https://doi.org/10.1609/aaai.v33i01.33015216