Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538692943
DOIs
StatePublished - Feb 11 2019
Event15th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2018 - Auckland, New Zealand
Duration: Nov 27 2018Nov 30 2018

Publication series

NameProceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance

Conference

Conference15th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2018
CountryNew Zealand
CityAuckland
Period11/27/1811/30/18

Fingerprint

Semantics

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Media Technology

Cite this

Li, Y., Yu, T., & Li, B. (2019). Simultaneous Event Localization and Recognition in Surveillance Video. In Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance [8639169] (Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AVSS.2018.8639169

Simultaneous Event Localization and Recognition in Surveillance Video. / Li, Yikang; Yu, Tianshu; Li, Baoxin.

Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance. Institute of Electrical and Electronics Engineers Inc., 2019. 8639169 (Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Li, Y, Yu, T & Li, B 2019, Simultaneous Event Localization and Recognition in Surveillance Video. in Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance., 8639169, Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance, Institute of Electrical and Electronics Engineers Inc., 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2018, Auckland, New Zealand, 11/27/18. https://doi.org/10.1109/AVSS.2018.8639169
Li Y, Yu T, Li B. Simultaneous Event Localization and Recognition in Surveillance Video. In Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance. Institute of Electrical and Electronics Engineers Inc. 2019. 8639169. (Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance). https://doi.org/10.1109/AVSS.2018.8639169
Li, Yikang ; Yu, Tianshu ; Li, Baoxin. / Simultaneous Event Localization and Recognition in Surveillance Video. Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance. Institute of Electrical and Electronics Engineers Inc., 2019. (Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance).
@inproceedings{dce1d463bf8444d885e9490dc7cb26a7,
title = "Simultaneous Event Localization and Recognition in Surveillance Video",
abstract = "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.",
author = "Yikang Li and Tianshu Yu and Baoxin Li",
year = "2019",
month = "2",
day = "11",
doi = "10.1109/AVSS.2018.8639169",
language = "English (US)",
series = "Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance",

}

TY - GEN

T1 - Simultaneous Event Localization and Recognition in Surveillance Video

AU - Li, Yikang

AU - Yu, Tianshu

AU - Li, Baoxin

PY - 2019/2/11

Y1 - 2019/2/11

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

UR - http://www.scopus.com/inward/citedby.url?scp=85063264032&partnerID=8YFLogxK

U2 - 10.1109/AVSS.2018.8639169

DO - 10.1109/AVSS.2018.8639169

M3 - Conference contribution

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.

ER -