Please use this identifier to cite or link to this item:
Title: A Ulam's game for video based action recognition
Authors: Zheng, Haofeng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Zheng, H. (2022). A Ulam's game for video based action recognition. Master's thesis, Nanyang Technological University, Singapore.
Project: ISM-DISS-02979
Abstract: In this dissertation, we propose a conditional early exiting framework with Ulam’s Game for action recognition. Since the action recognition system has extremely high requirements on dynamic performance, our system pays more attention to improving the detection efficiency of the system, hoping to obtain the detection results in a shorter time. In our system, we use a modified ResNet-50 as backbone network to do feature extraction and use a Pooling module to accumulate feature. Then, we have a neural network Gate module to determine whether the feature have accumulated enough to begin Ulam’s Game. A classifier is used to get candidate results, which are used to run Ulam’s Game and get the final prediction. The model shows good detection accuracy and dynamic performance in multiple data sets (Mini-Kinetics, ActivityNet).
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
A Ulam's Game for Video Based Action Recognition.pdf
  Restricted Access
9.96 MBAdobe PDFView/Open

Page view(s)

Updated on Sep 26, 2023


Updated on Sep 26, 2023

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.