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.
|Title of host publication||2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021|
|Number of pages||9|
|Publication status||Published - 2021|
|Event||18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021 - Virtual, Online, Canada|
Duration: 11 Oct 2021 → 17 Oct 2021
|Conference||18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021|
|Period||11/10/2021 → 17/10/2021|
|Series||IEEE International Conference on Computer Vision Workshops (ICCVW)|
Bibliographical noteFunding Information:
This work was supported by the Milestone Research Programme at Aalborg University (MRPA), by the Spanish projectPID2019-105093GB-I00(MINECO/FEDER,UE), andbyICREAundertheICREAAcademiaprogramme.
© 2021 IEEE.