TY  - CONF
AU  - Neelu Madan
AU  - Arya Farkhondeh
AU  - Kamal Nasrollahi
AU  - Sergio Escalera
AU  - Thomas B. Moeslund
A2  - ICCVW
PY  - 2021//
TI  - Temporal Cues From Socially Unacceptable Trajectories for Anomaly Detection
BT  - IEEE/CVF International Conference on Computer Vision Workshops
SP  - 2150
EP  - 2158
N2  - State-of-the-Art (SoTA) deep learning-based approaches to detect anomalies in surveillance videos utilize limited temporal information, including basic information from motion, e.g., optical flow computed between consecutive frames. In this paper, we compliment the SoTA methods by including long-range dependencies from trajectories for anomaly detection. To achieve that, we first created trajectories by running a tracker on two SoTA datasets, namely Avenue and Shanghai-Tech. We propose a prediction-based anomaly detection method using trajectories based on Social GANs, also called in this paper as temporal-based anomaly detection. Then, we hypothesize that late fusion of the result of this temporal-based anomaly detection system with spatial-based anomaly detection systems produces SoTA results. We verify this hypothesis on two spatial-based anomaly detection systems. We show that both cases produce results better than baseline spatial-based systems, indicating the usefulness of the temporal information coming from the trajectories for anomaly detection. We observe that the proposed approach depicts the maximum improvement in micro-level Area-Under-the-Curve (AUC) by 4.1% on CUHK Avenue and 3.4% on Shanghai-Tech over one of the baseline method. We also show a high performance on cross-data evaluation, where we learn the weights to combine spatial and temporal information on Shanghai-Tech and perform evaluation on CUHK Avenue and vice-versa.
L1  - http://refbase.cvc.uab.es/files/.pdf
N1  - HUPBA; no proj
ID  - Neelu Madan2021
ER  -