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State-of-the-Art (SoTA) deep learning-based ap-proaches to detect anomalies utilize limited temporal in-formation, including basic information from motion, e.g.,optical flow computed between consecutive frames. In thispaper, we compliment the SoTA methods by including long-range dependencies from trajectories for anomaly detec-tion. To achieve that, we first created trajectories by run-ning a tracker on two SoTA datasets, namely Avenue andShanghai-Tech. We propose a prediction-based anomalydetection method using trajectories based on Social GANs,also called in this paper as temporal-based anomaly de-tection. Then, we hypothesize that late fusion of the re-sult of this temporal-based anomaly detection system withspatial-based anomaly detection systems produces SoTAresults. We verify this hypothesis on two spatial-basedanomaly detection systems. We show that both cases pro-duce results better than baseline spatial-based systems, in-dicating the usefulness of the temporal information com-ing from the trajectories for anomaly detection. We observethat the proposed approach depicts the maximum improve-ment in micro-level Area-Under-the-Curve (AUC) by 4.1%on CUHK Avenue and 3.4% on Shanghai-Tech over one ofthe baseline method. We also show a high performance oncross-data evaluation, where we learn the weights to com-bine spatial and temporal information on Shanghai-Techand perform evaluation on CUHK Avenue and vice-versa.
Original languageEnglish
Title of host publication2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
Number of pages9
Publication date2021
Article number9607434
ISBN (Print)978-1-6654-0192-0
ISBN (Electronic)9781665401913
Publication statusPublished - 2021
Event18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021 - Virtual, Online, Canada
Duration: 11 Oct 202117 Oct 2021


Conference18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
CityVirtual, Online
SponsorCVF, IEEE
SeriesIEEE International Conference on Computer Vision Workshops (ICCVW)

Bibliographical note

Funding Information:
This work was supported by the Milestone Research Programme at Aalborg University (MRPA), by the Spanish projectPID2019-105093GB-I00(MINECO/FEDER,UE), andbyICREAundertheICREAAcademiaprogramme.

Publisher Copyright:
© 2021 IEEE.


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