Human action recognition from video sequences
Umakanthan, Sabanadesan (2016) Human action recognition from video sequences. PhD thesis, Queensland University of Technology.
This PhD research has proposed new machine learning techniques to improve human action recognition based on local features. Several novel video representation and classification techniques have been proposed to increase the performance with lower computational complexity. The major contributions are the construction of new feature representation techniques, based on advanced machine learning techniques such as multiple instance dictionary learning, Latent Dirichlet Allocation (LDA) and Sparse coding. A Binary-tree based classification technique was also proposed to deal with large amounts of action categories. These techniques are not only improving the classification accuracy with constrained computational resources but are also robust to challenging environmental conditions. These developed techniques can be easily extended to a wide range of video applications to provide near real-time performance.
Impact and interest:
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|Item Type:||QUT Thesis (PhD)|
|Supervisor:||Fookes, Clinton, Sridharan, Sridha, & Denman, Simon|
|Keywords:||Action Recognition, Human Motion Analysis, Local Spatio-Temporal Features, Sparse Representation, LDA Representation, Multiple Instance Dictionary Learning|
|Institution:||Queensland University of Technology|
|Deposited On:||21 Apr 2016 01:59|
|Last Modified:||21 Apr 2016 01:59|
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