Leveraging Textural Features for Recognizing Actions in Low Quality Videos


Rahman, Saimunur and See, John Su Yang and Ho, Chiung Ching (2016) Leveraging Textural Features for Recognizing Actions in Low Quality Videos. In: 9th International Conference on Robotic, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, 398 . Springer, pp. 237-245. ISBN 978-981-10-1719-3, 978-981-10-1721-6

[img] Text
Restricted to Repository staff only

Download (1MB)


Human action recognition is a well researched problem, which is considerably more challenging when video quality is poor. In this paper, we investigate human action recognition in low quality videos by leveraging the robustness of textural features to better characterize actions, instead of relying on shape and motion features may fail under noisy conditions. To accommodate videos, texture descriptors are extended to three orthogonal planes (TOP) to extract spatio-temporal features. Extensive experiments were conducted on low quality versions of the KTH and HMDB51 datasets to evaluate the performance of our proposed approaches against standard baselines. Experimental results and further analysis demonstrated the usefulness of textural features in improving the capability of recognizing human actions from low quality videos.

Item Type: Book Section
Uncontrolled Keywords: Human activity recognition, Human Action Recognition
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 29 Nov 2020 10:09
Last Modified: 29 Nov 2020 10:09
URII: http://shdl.mmu.edu.my/id/eprint/7121


Downloads per month over past year

View ItemEdit (login required)