TY - GEN
T1 - Simultaneous Event Localization and Recognition in Surveillance Video
AU - Li, Yikang
AU - Yu, Tianshu
AU - Li, Baoxin
N1 - Funding Information:
This work was supported in part by a grant from ONR. Any opinions expressed in this material are those of the authors and do not necessarily react the views of ONR.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - The ubiquity of video-based surveillance demands automated approaches to analysis of ever-increasing video footages. Action/Event localization and recognition are two critical capabilities in surveillance video analysis, which have been largely addressed separately in the literature. In this paper, we propose an approach to simultaneously localize and recognize visual events from raw surveillance videos, employing an end-to-end learning strategy. Our approach formulates the task as weakly-supervised sequential semantic segmentation, in which we utilize a specific convolutional RNN to capture not only the appearance and the motion information but also their temporal evolution patterns. We tested our approach on the VIRAT 2.0 dataset. The experimental results, in comparison with relevant existing state-of-the-art, suggest that the proposed approach is promising in delivering a practical solution.
AB - The ubiquity of video-based surveillance demands automated approaches to analysis of ever-increasing video footages. Action/Event localization and recognition are two critical capabilities in surveillance video analysis, which have been largely addressed separately in the literature. In this paper, we propose an approach to simultaneously localize and recognize visual events from raw surveillance videos, employing an end-to-end learning strategy. Our approach formulates the task as weakly-supervised sequential semantic segmentation, in which we utilize a specific convolutional RNN to capture not only the appearance and the motion information but also their temporal evolution patterns. We tested our approach on the VIRAT 2.0 dataset. The experimental results, in comparison with relevant existing state-of-the-art, suggest that the proposed approach is promising in delivering a practical solution.
UR - http://www.scopus.com/inward/record.url?scp=85063264032&partnerID=8YFLogxK
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U2 - 10.1109/AVSS.2018.8639169
DO - 10.1109/AVSS.2018.8639169
M3 - Conference contribution
AN - SCOPUS:85063264032
T3 - Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance
BT - Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2018
Y2 - 27 November 2018 through 30 November 2018
ER -