Empirical Study on the Tradeoffs of Action Recognition Models for Industry

Action recognition has attracted intense attention in the last decade. Advances in deep learning and the availability of large-scale video datasets have drastically improved its capabilities, attracting interest from industry with a variety of use cases. My work at the MIT-IBM Watson AI lab presents a survey analyzing the performance of various existing action recognition methods in the context of construction site analysis and workplace safety. The analyzed pretrained action recognition models were developed in the lab, and encompass a range of popular techniques in the field, each with their own strengths. In addition to developing a general pipeline to train these models on novel datasets, an easy-to-follow guide is presented to make model recommendations for both of the construction site and workplace safety tasks. Although it was hard to make definitive recommendations without knowing the specific hardware constraints, the results obtained and the discussion offer insight into what it feasible and effective with current technology in the problem spaces.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Snowdon, Jack

Advisors dc:contributor.advisor

• Oliva, Aude
• Martie, Lee

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/145033

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/145033