Action recognition in the dark via deep representation learning

Research output: Book chapter/Published conference paperConference paper

Abstract

Human action recognition for automated video surveillance applications is an interesting but a daunting task especially if the videos are captured in unfavourable lighting conditions. These situations encourage the use of multi-sensor video streams. However, simultaneous activity recognition from multiple video streams is a difficult problem due to their complementary and noisy nature. This paper proposes simultaneous action recognition from multiple video streams using deep multi-view representation learning. Furthermore, it introduces a spatio-temporal feature based correlation filter, for simultaneous detection and recognition of multiple human actions in low-light conditions. We evaluated the performance of our proposed filter with extensive experimentation on nighttime action datasets. Experimental results indicate the effectiveness of deep fusion scheme for robust action recognition in extremely low-light conditions.
Original languageEnglish
Title of host publicationIEEE 3rd International Conference on Image Processing, Applications and Systems, IPAS 2018
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages131-136
Number of pages6
ISBN (Electronic)9781728102474
ISBN (Print)9781728102481
DOIs
Publication statusPublished - 06 May 2019
Event3rd IEEE International Conference on Image Processing, Applications and Systems, IPAS 2018 - Inria Sophia Antipolis, Sophia Antipolis, France
Duration: 12 Dec 201814 Dec 2018
https://ipas.ieee.tn/ (conference website)

Conference

Conference3rd IEEE International Conference on Image Processing, Applications and Systems, IPAS 2018
CountryFrance
CitySophia Antipolis
Period12/12/1814/12/18
Internet address

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  • Cite this

    Ulhaq, A. (2019). Action recognition in the dark via deep representation learning. In IEEE 3rd International Conference on Image Processing, Applications and Systems, IPAS 2018 (pp. 131-136). [8708903] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IPAS.2018.8708903