TY - GEN
T1 - Efficient unsupervised abnormal crowd activity detection based on a spatiotemporal saliency detector
AU - Wang, Yilin
AU - Zhang, Qiang
AU - Li, Baoxin
PY - 2016/5/23
Y1 - 2016/5/23
N2 - Approaches to abnormality detection in crowded scene largely rely on supervised methods using discriminative models. In this paper, we presents a novel and efficient unsupervised learning method for video analysis. We start from visual saliency, which has been used in several vision tasks, e.g., image classification, object detection, and foreground segmentation. To detect saliency regions in video sequences, we propose a new approach for detecting spatiotemporal visual saliency based on the phase spectrum of the videos, which is easy to implement and computationally efficient. With the proposed algorithm, we also study how the spatiotemporal saliency can be used in two important vision tasks, saliency prediction and abnormality detection. The proposed algorithm is evaluated on several benchmark datasets with comparison to the state-of-the-art methods from the literature. The experiments demonstrate the effectiveness of the proposed approach to spatiotemporal visual saliency detection and its application to the above vision tasks.
AB - Approaches to abnormality detection in crowded scene largely rely on supervised methods using discriminative models. In this paper, we presents a novel and efficient unsupervised learning method for video analysis. We start from visual saliency, which has been used in several vision tasks, e.g., image classification, object detection, and foreground segmentation. To detect saliency regions in video sequences, we propose a new approach for detecting spatiotemporal visual saliency based on the phase spectrum of the videos, which is easy to implement and computationally efficient. With the proposed algorithm, we also study how the spatiotemporal saliency can be used in two important vision tasks, saliency prediction and abnormality detection. The proposed algorithm is evaluated on several benchmark datasets with comparison to the state-of-the-art methods from the literature. The experiments demonstrate the effectiveness of the proposed approach to spatiotemporal visual saliency detection and its application to the above vision tasks.
UR - http://www.scopus.com/inward/record.url?scp=84977609088&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84977609088&partnerID=8YFLogxK
U2 - 10.1109/WACV.2016.7477684
DO - 10.1109/WACV.2016.7477684
M3 - Conference contribution
T3 - 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
BT - 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Winter Conference on Applications of Computer Vision, WACV 2016
Y2 - 7 March 2016 through 10 March 2016
ER -