TY - GEN
T1 - Online weighted clustering for real-time abnormal event detection in video surveillance
AU - Lin, Hanhe
AU - Deng, Jeremiah D.
AU - Woodford, Brendon J.
AU - Shahi, Ahmad
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/10/1
Y1 - 2016/10/1
N2 - Detecting abnormal events in video surveillance is a challenging problem due to the large scale, stream fashion video data as well as the real-time constraint. In this paper, we present an online, adaptive, and real-time framework to address this problem. The spatial locations in a frame is partitioned into grids, in each grid the proposed Adaptive Multi-scale Histogram Optical Flow (AMHOF) features are extracted and modelled by an Online Weighted Clustering (OWC) algorithm. The AMHOFs which cannot be fit to a cluster with large weight are regarded as abnormal events. The OWC algorithm is simple to implement and computational efficient. In addition, we improve the detection performance by a Multiple Target Tracking (MTT) algorithm. Experimental results demonstrate our approach outperforms the state-of-the-art approaches in pixel-level rate of detection at a processing speed of 30 FPS.
AB - Detecting abnormal events in video surveillance is a challenging problem due to the large scale, stream fashion video data as well as the real-time constraint. In this paper, we present an online, adaptive, and real-time framework to address this problem. The spatial locations in a frame is partitioned into grids, in each grid the proposed Adaptive Multi-scale Histogram Optical Flow (AMHOF) features are extracted and modelled by an Online Weighted Clustering (OWC) algorithm. The AMHOFs which cannot be fit to a cluster with large weight are regarded as abnormal events. The OWC algorithm is simple to implement and computational efficient. In addition, we improve the detection performance by a Multiple Target Tracking (MTT) algorithm. Experimental results demonstrate our approach outperforms the state-of-the-art approaches in pixel-level rate of detection at a processing speed of 30 FPS.
KW - Abnormal event detection
KW - Multiple target tracking
KW - Online adaptive learning
KW - Real-time
KW - Video surveillance
UR - http://www.scopus.com/inward/record.url?scp=84994635711&partnerID=8YFLogxK
U2 - 10.1145/2964284.2967279
DO - 10.1145/2964284.2967279
M3 - Conference contribution
AN - SCOPUS:84994635711
T3 - MM 2016 - Proceedings of the 2016 ACM Multimedia Conference
SP - 536
EP - 540
BT - MM 2016
PB - Association for Computing Machinery
CY - New York, NY
T2 - 24th ACM Multimedia Conference, MM 2016
Y2 - 15 October 2016 through 19 October 2016
ER -