Action recognition

from static datasets to moving robots

Fahimeh Rezazadegan, Sareh Shirazi, Ben Upcrofit, Michael Milford

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

8 Citations (Scopus)

Abstract

Deep learning models have achieved state-of-the-art performance in recognizing human activities, but often rely on utilizing background cues present in typical computer vision datasets that predominantly have a stationary camera. If these models are to be employed by autonomous robots in real world environments, they must be adapted to perform independently of background cues and camera motion effects. To address these challenges, we propose a new method that firstly generates generic action region proposals with good potential to locate one human action in unconstrained videos regardless of camera motion and then uses action proposals to extract and classify effective shape and motion features by a ConvNet framework. In a range of experiments, we demonstrate that by actively proposing action regions during both training and testing, state-of-the-art or better performance is achieved on benchmarks. We show the outperformance of our approach compared to the state-of-the-art in two new datasets; one emphasizes on irrelevant background, the other highlights the camera motion. We also validate our action recognition method in an abnormal behavior detection scenario to improve workplace safety. The results verify a higher success rate for our method due to the ability of our system to recognize human actions regardless of environment and camera motion.

Original languageEnglish
Title of host publicationICRA 2017 - IEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages3185-3191
Number of pages7
ISBN (Electronic)9781509046331
ISBN (Print)9781509046348
DOIs
Publication statusPublished - 21 Jul 2017
Externally publishedYes
Event2017 IEEE International Conference on Robotics and Automation, ICRA 2017 - Singapore, Singapore
Duration: 29 May 20173 Jun 2017

Conference

Conference2017 IEEE International Conference on Robotics and Automation, ICRA 2017
CountrySingapore
CitySingapore
Period29/05/173/06/17

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Cite this

Rezazadegan, F., Shirazi, S., Upcrofit, B., & Milford, M. (2017). Action recognition: from static datasets to moving robots. In ICRA 2017 - IEEE International Conference on Robotics and Automation (pp. 3185-3191). [7989361] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICRA.2017.7989361