TY - GEN
T1 - Video Anomaly Detection for Surveillance Based on Effective Frame Area
AU - Yang, Yuxing
AU - Xian, Yang
AU - Fu, Zeyu
AU - Naqvi, Syed Mohsen
N1 - Publisher Copyright:
© 2021 International Society of Information Fusion (ISIF).
PY - 2021
Y1 - 2021
N2 - Video anomaly detection aims to recognise and analyse the video sequences to classify the normal and abnormal frames. This technology can efficiently reduce the human labour to discover the anomalies in surveillance systems and is widely applied in financial, public security and transport sectors. However, video anomaly detection performance is often degraded by the dataset quality, especially for small objects in video sequences. Besides, the computational cost of the classification model would be required as low as possible. In this paper, we proposed information fusion with a joint model which contains motion estimation, object detection and adversarial learning to detect anomalies in two video datasets: UCSD PED1 and PED2. Experimental results confirm the proposed method outperforms the state-of-the-art methods with the additional advantages in reduced computation cost.
AB - Video anomaly detection aims to recognise and analyse the video sequences to classify the normal and abnormal frames. This technology can efficiently reduce the human labour to discover the anomalies in surveillance systems and is widely applied in financial, public security and transport sectors. However, video anomaly detection performance is often degraded by the dataset quality, especially for small objects in video sequences. Besides, the computational cost of the classification model would be required as low as possible. In this paper, we proposed information fusion with a joint model which contains motion estimation, object detection and adversarial learning to detect anomalies in two video datasets: UCSD PED1 and PED2. Experimental results confirm the proposed method outperforms the state-of-the-art methods with the additional advantages in reduced computation cost.
KW - Generative Adversarial Networks
KW - Motion Estimation
KW - Object Detection
KW - Video Anomaly Detection
UR - http://www.scopus.com/inward/record.url?scp=85123432922&partnerID=8YFLogxK
M3 - Conference Proceeding
AN - SCOPUS:85123432922
T3 - Proceedings of 2021 IEEE 24th International Conference on Information Fusion, FUSION 2021
BT - Proceedings of 2021 IEEE 24th International Conference on Information Fusion, FUSION 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th IEEE International Conference on Information Fusion, FUSION 2021
Y2 - 1 November 2021 through 4 November 2021
ER -